From 5aca65139ea89b53a6ea2dd9a7dd3b7829f4cc58 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Mon, 5 Oct 2020 03:19:18 +0000 Subject: [PATCH 01/24] tra_analysis v 2.1.0-alpha.1 Signed-off-by: Arthur Lu --- .../equation-checkpoint.ipynb | 35 - analysis-master/tra_analysis/Analysis.py | 648 +++++++ analysis-master/tra_analysis/Array.py | 170 ++ .../tra_analysis/ClassificationMetric.py | 36 + .../tra_analysis/CorrelationTest.py | 58 + .../tra_analysis/CorrelationTest_obj.py | 41 + .../tra_analysis/{fits.py => Fit.py} | 4 +- analysis-master/tra_analysis/KNN.py | 42 + analysis-master/tra_analysis/KNN_obj.py | 25 + analysis-master/tra_analysis/NaiveBayes.py | 59 + .../tra_analysis/NaiveBayes_obj.py | 43 + analysis-master/tra_analysis/RandomForest.py | 42 + .../tra_analysis/RandomForest_obj.py | 25 + .../tra_analysis/RegressionMetric.py | 40 + analysis-master/tra_analysis/SVM.py | 79 + analysis-master/tra_analysis/Sort.py | 408 +++++ analysis-master/tra_analysis/Sort_obj.py | 391 +++++ .../tra_analysis/StatisticalTest.py | 187 ++ .../tra_analysis/StatisticalTest_obj.py | 170 ++ analysis-master/tra_analysis/__init__.py | 39 + analysis-master/tra_analysis/analysis.py | 1531 ----------------- analysis-master/tra_analysis/equation.ipynb | 162 -- .../{regression.py => regression_old.py} | 0 23 files changed, 2506 insertions(+), 1729 deletions(-) delete mode 100644 analysis-master/tra_analysis/.ipynb_checkpoints/equation-checkpoint.ipynb create mode 100644 analysis-master/tra_analysis/Analysis.py create mode 100644 analysis-master/tra_analysis/Array.py create mode 100644 analysis-master/tra_analysis/ClassificationMetric.py create mode 100644 analysis-master/tra_analysis/CorrelationTest.py create mode 100644 analysis-master/tra_analysis/CorrelationTest_obj.py rename analysis-master/tra_analysis/{fits.py => Fit.py} (96%) create mode 100644 analysis-master/tra_analysis/KNN.py create mode 100644 analysis-master/tra_analysis/KNN_obj.py create mode 100644 analysis-master/tra_analysis/NaiveBayes.py create mode 100644 analysis-master/tra_analysis/NaiveBayes_obj.py create mode 100644 analysis-master/tra_analysis/RandomForest.py create mode 100644 analysis-master/tra_analysis/RandomForest_obj.py create mode 100644 analysis-master/tra_analysis/RegressionMetric.py create mode 100644 analysis-master/tra_analysis/SVM.py create mode 100644 analysis-master/tra_analysis/Sort.py create mode 100644 analysis-master/tra_analysis/Sort_obj.py create mode 100644 analysis-master/tra_analysis/StatisticalTest.py create mode 100644 analysis-master/tra_analysis/StatisticalTest_obj.py delete mode 100644 analysis-master/tra_analysis/analysis.py delete mode 100644 analysis-master/tra_analysis/equation.ipynb rename analysis-master/tra_analysis/{regression.py => regression_old.py} (100%) diff --git a/analysis-master/tra_analysis/.ipynb_checkpoints/equation-checkpoint.ipynb b/analysis-master/tra_analysis/.ipynb_checkpoints/equation-checkpoint.ipynb deleted file mode 100644 index 9245ab26..00000000 --- a/analysis-master/tra_analysis/.ipynb_checkpoints/equation-checkpoint.ipynb +++ /dev/null @@ -1,35 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "string = \"3+4+5\"\n", - "re.sub(\"\\d+[+]{1}\\d+\", string, sum([int(i) for i in re.split(\"[+]{1}\", re.search(\"\\d+[+]{1}\\d+\", string).group())]))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/analysis-master/tra_analysis/Analysis.py b/analysis-master/tra_analysis/Analysis.py new file mode 100644 index 00000000..d60a79ad --- /dev/null +++ b/analysis-master/tra_analysis/Analysis.py @@ -0,0 +1,648 @@ +# Titan Robotics Team 2022: Analysis Module +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import Analysis' +# this should be included in the local directory or environment variable +# this module has been optimized for multhreaded computing +# current benchmark of optimization: 1.33 times faster +# setup: + +__version__ = "3.0.0" + +# changelog should be viewed using print(analysis.__changelog__) +__changelog__ = """changelog: + 3.0.0: + - exported several submodules to their own files while preserving backwards compatibility: + - Array + - ClassificationMetric + - CorrelationTest + - KNN + - NaiveBayes + - RandomForest + - RegressionMetric + - Sort + - StatisticalTest + - SVM + - note: above listed submodules will not be supported in the future + - future changes to all submodules will be held in their respective changelogs + - future changes altering the parent package will be held in the __changelog__ of the parent package (in __init__.py) + - changed reference to module name to Analysis + 2.3.1: + - fixed bugs in Array class + 2.3.0: + - overhauled Array class + 2.2.3: + - fixed spelling of RandomForest + - made n_neighbors required for KNN + - made n_classifiers required for SVM + 2.2.2: + - fixed 2.2.1 changelog entry + - changed regression to return dictionary + 2.2.1: + - changed all references to parent package analysis to tra_analysis + 2.2.0: + - added Sort class + - added several array sorting functions to Sort class including: + - quick sort + - merge sort + - intro(spective) sort + - heap sort + - insertion sort + - tim sort + - selection sort + - bubble sort + - cycle sort + - cocktail sort + - tested all sorting algorithms with both lists and numpy arrays + - depreciated sort function from Array class + - added warnings as an import + 2.1.4: + - added sort and search functions to Array class + 2.1.3: + - changed output of basic_stats and histo_analysis to libraries + - fixed __all__ + 2.1.2: + - renamed ArrayTest class to Array + 2.1.1: + - added add, mul, neg, and inv functions to ArrayTest class + - added normalize function to ArrayTest class + - added dot and cross functions to ArrayTest class + 2.1.0: + - added ArrayTest class + - added elementwise mean, median, standard deviation, variance, min, max functions to ArrayTest class + - added elementwise_stats to ArrayTest which encapsulates elementwise statistics + - appended to __all__ to reflect changes + 2.0.6: + - renamed func functions in regression to lin, log, exp, and sig + 2.0.5: + - moved random_forrest_regressor and random_forrest_classifier to RandomForrest class + - renamed Metrics to Metric + - renamed RegressionMetrics to RegressionMetric + - renamed ClassificationMetrics to ClassificationMetric + - renamed CorrelationTests to CorrelationTest + - renamed StatisticalTests to StatisticalTest + - reflected rafactoring to all mentions of above classes/functions + 2.0.4: + - fixed __all__ to reflected the correct functions and classes + - fixed CorrelationTests and StatisticalTests class functions to require self invocation + - added missing math import + - fixed KNN class functions to require self invocation + - fixed Metrics class functions to require self invocation + - various spelling fixes in CorrelationTests and StatisticalTests + 2.0.3: + - bug fixes with CorrelationTests and StatisticalTests + - moved glicko2 and trueskill to the metrics subpackage + - moved elo to a new metrics subpackage + 2.0.2: + - fixed docs + 2.0.1: + - fixed docs + 2.0.0: + - cleaned up wild card imports with scipy and sklearn + - added CorrelationTests class + - added StatisticalTests class + - added several correlation tests to CorrelationTests + - added several statistical tests to StatisticalTests + 1.13.9: + - moved elo, glicko2, trueskill functions under class Metrics + 1.13.8: + - moved Glicko2 to a seperate package + 1.13.7: + - fixed bug with trueskill + 1.13.6: + - cleaned up imports + 1.13.5: + - cleaned up package + 1.13.4: + - small fixes to regression to improve performance + 1.13.3: + - filtered nans from regression + 1.13.2: + - removed torch requirement, and moved Regression back to regression.py + 1.13.1: + - bug fix with linear regression not returning a proper value + - cleaned up regression + - fixed bug with polynomial regressions + 1.13.0: + - fixed all regressions to now properly work + 1.12.6: + - fixed bg with a division by zero in histo_analysis + 1.12.5: + - fixed numba issues by removing numba from elo, glicko2 and trueskill + 1.12.4: + - renamed gliko to glicko + 1.12.3: + - removed depreciated code + 1.12.2: + - removed team first time trueskill instantiation in favor of integration in superscript.py + 1.12.1: + - improved readibility of regression outputs by stripping tensor data + - used map with lambda to acheive the improved readibility + - lost numba jit support with regression, and generated_jit hangs at execution + - TODO: reimplement correct numba integration in regression + 1.12.0: + - temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures + 1.11.010: + - alphabeticaly ordered import lists + 1.11.9: + - bug fixes + 1.11.8: + - bug fixes + 1.11.7: + - bug fixes + 1.11.6: + - tested min and max + - bug fixes + 1.11.5: + - added min and max in basic_stats + 1.11.4: + - bug fixes + 1.11.3: + - bug fixes + 1.11.2: + - consolidated metrics + - fixed __all__ + 1.11.1: + - added test/train split to RandomForestClassifier and RandomForestRegressor + 1.11.0: + - added RandomForestClassifier and RandomForestRegressor + - note: untested + 1.10.0: + - added numba.jit to remaining functions + 1.9.2: + - kernelized PCA and KNN + 1.9.1: + - fixed bugs with SVM and NaiveBayes + 1.9.0: + - added SVM class, subclasses, and functions + - note: untested + 1.8.0: + - added NaiveBayes classification engine + - note: untested + 1.7.0: + - added knn() + - added confusion matrix to decisiontree() + 1.6.2: + - changed layout of __changelog to be vscode friendly + 1.6.1: + - added additional hyperparameters to decisiontree() + 1.6.0: + - fixed __version__ + - fixed __all__ order + - added decisiontree() + 1.5.3: + - added pca + 1.5.2: + - reduced import list + - added kmeans clustering engine + 1.5.1: + - simplified regression by using .to(device) + 1.5.0: + - added polynomial regression to regression(); untested + 1.4.0: + - added trueskill() + 1.3.2: + - renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2 + 1.3.1: + - changed glicko2() to return tuple instead of array + 1.3.0: + - added glicko2_engine class and glicko() + - verified glicko2() accuracy + 1.2.3: + - fixed elo() + 1.2.2: + - added elo() + - elo() has bugs to be fixed + 1.2.1: + - readded regrression import + 1.2.0: + - integrated regression.py as regression class + - removed regression import + - fixed metadata for regression class + - fixed metadata for analysis class + 1.1.1: + - regression_engine() bug fixes, now actaully regresses + 1.1.0: + - added regression_engine() + - added all regressions except polynomial + 1.0.7: + - updated _init_device() + 1.0.6: + - removed useless try statements + 1.0.5: + - removed impossible outcomes + 1.0.4: + - added performance metrics (r^2, mse, rms) + 1.0.3: + - resolved nopython mode for mean, median, stdev, variance + 1.0.2: + - snapped (removed) majority of uneeded imports + - forced object mode (bad) on all jit + - TODO: stop numba complaining about not being able to compile in nopython mode + 1.0.1: + - removed from sklearn import * to resolve uneeded wildcard imports + 1.0.0: + - removed c_entities,nc_entities,obstacles,objectives from __all__ + - applied numba.jit to all functions + - depreciated and removed stdev_z_split + - cleaned up histo_analysis to include numpy and numba.jit optimizations + - depreciated and removed all regression functions in favor of future pytorch optimizer + - depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data) + - optimized z_normalize using sklearn.preprocessing.normalize + - TODO: implement kernel/function based pytorch regression optimizer + 0.9.0: + - refactored + - numpyed everything + - removed stats in favor of numpy functions + 0.8.5: + - minor fixes + 0.8.4: + - removed a few unused dependencies + 0.8.3: + - added p_value function + 0.8.2: + - updated __all__ correctly to contain changes made in v 0.8.0 and v 0.8.1 + 0.8.1: + - refactors + - bugfixes + 0.8.0: + - depreciated histo_analysis_old + - depreciated debug + - altered basic_analysis to take array data instead of filepath + - refactor + - optimization + 0.7.2: + - bug fixes + 0.7.1: + - bug fixes + 0.7.0: + - added tanh_regression (logistical regression) + - bug fixes + 0.6.5: + - added z_normalize function to normalize dataset + - bug fixes + 0.6.4: + - bug fixes + 0.6.3: + - bug fixes + 0.6.2: + - bug fixes + 0.6.1: + - corrected __all__ to contain all of the functions + 0.6.0: + - added calc_overfit, which calculates two measures of overfit, error and performance + - added calculating overfit to optimize_regression + 0.5.0: + - added optimize_regression function, which is a sample function to find the optimal regressions + - optimize_regression function filters out some overfit funtions (functions with r^2 = 1) + - planned addition: overfit detection in the optimize_regression function + 0.4.2: + - added __changelog__ + - updated debug function with log and exponential regressions + 0.4.1: + - added log regressions + - added exponential regressions + - added log_regression and exp_regression to __all__ + 0.3.8: + - added debug function to further consolidate functions + 0.3.7: + - added builtin benchmark function + - added builtin random (linear) data generation function + - added device initialization (_init_device) + 0.3.6: + - reorganized the imports list to be in alphabetical order + - added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives + 0.3.5: + - major bug fixes + - updated historical analysis + - depreciated old historical analysis + 0.3.4: + - added __version__, __author__, __all__ + - added polynomial regression + - added root mean squared function + - added r squared function + 0.3.3: + - bug fixes + - added c_entities + 0.3.2: + - bug fixes + - added nc_entities, obstacles, objectives + - consolidated statistics.py to analysis.py + 0.3.1: + - compiled 1d, column, and row basic stats into basic stats function + 0.3.0: + - added historical analysis function + 0.2.x: + - added z score test + 0.1.x: + - major bug fixes + 0.0.x: + - added loading csv + - added 1d, column, row basic stats +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ + 'load_csv', + 'basic_stats', + 'z_score', + 'z_normalize', + 'histo_analysis', + 'regression', + 'Metric', + 'RegressionMetric', + 'ClassificationMetric', + 'kmeans', + 'pca', + 'decisiontree', + 'KNN', + 'NaiveBayes', + 'SVM', + 'RandomForrest', + 'CorrelationTest', + 'StatisticalTest', + 'Array', + # all statistics functions left out due to integration in other functions +] + +# now back to your regularly scheduled programming: + +# imports (now in alphabetical order! v 0.3.006): + +import csv +from tra_analysis.metrics import elo as Elo +from tra_analysis.metrics import glicko2 as Glicko2 +import math +import numba +from numba import jit +import numpy as np +import scipy +from scipy import optimize, stats +import sklearn +from sklearn import preprocessing, pipeline, linear_model, metrics, cluster, decomposition, tree, neighbors, naive_bayes, svm, model_selection, ensemble +from tra_analysis.metrics import trueskill as Trueskill +import warnings + +# import submodules + +from .Array import Array +from .ClassificationMetric import ClassificationMetric +from .CorrelationTest_obj import CorrelationTest +from .KNN_obj import KNN +from .NaiveBayes_obj import NaiveBayes +from .RandomForest_obj import RandomForest +from .RegressionMetric import RegressionMetric +from .Sort_obj import Sort +from .StatisticalTest_obj import StatisticalTest +from .SVM import SVM + +class error(ValueError): + pass + +def load_csv(filepath): + with open(filepath, newline='') as csvfile: + file_array = np.array(list(csv.reader(csvfile))) + csvfile.close() + return file_array + +# expects 1d array +@jit(forceobj=True) +def basic_stats(data): + + data_t = np.array(data).astype(float) + + _mean = mean(data_t) + _median = median(data_t) + _stdev = stdev(data_t) + _variance = variance(data_t) + _min = npmin(data_t) + _max = npmax(data_t) + + return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max} + +# returns z score with inputs of point, mean and standard deviation of spread +@jit(forceobj=True) +def z_score(point, mean, stdev): + score = (point - mean) / stdev + + return score + +# expects 2d array, normalizes across all axes +@jit(forceobj=True) +def z_normalize(array, *args): + + array = np.array(array) + for arg in args: + array = sklearn.preprocessing.normalize(array, axis = arg) + + return array + +@jit(forceobj=True) +# expects 2d array of [x,y] +def histo_analysis(hist_data): + + if len(hist_data[0]) > 2: + + hist_data = np.array(hist_data) + derivative = np.array(len(hist_data) - 1, dtype = float) + t = np.diff(hist_data) + derivative = t[1] / t[0] + np.sort(derivative) + + return {"mean": basic_stats(derivative)["mean"], "deviation": basic_stats(derivative)["standard-deviation"]} + + else: + + return None + +def regression(inputs, outputs, args): # inputs, outputs expects N-D array + + X = np.array(inputs) + y = np.array(outputs) + + regressions = {} + + if 'lin' in args: # formula: ax + b + + try: + + def lin(x, a, b): + + return a * x + b + + popt, pcov = scipy.optimize.curve_fit(lin, X, y) + + coeffs = popt.flatten().tolist() + regressions["lin"] = (str(coeffs[0]) + "*x+" + str(coeffs[1])) + + except Exception as e: + + pass + + if 'log' in args: # formula: a log (b(x + c)) + d + + try: + + def log(x, a, b, c, d): + + return a * np.log(b*(x + c)) + d + + popt, pcov = scipy.optimize.curve_fit(log, X, y) + + coeffs = popt.flatten().tolist() + regressions["log"] = (str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3])) + + except Exception as e: + + pass + + if 'exp' in args: # formula: a e ^ (b(x + c)) + d + + try: + + def exp(x, a, b, c, d): + + return a * np.exp(b*(x + c)) + d + + popt, pcov = scipy.optimize.curve_fit(exp, X, y) + + coeffs = popt.flatten().tolist() + regressions["exp"] = (str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3])) + + except Exception as e: + + pass + + if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ... + + inputs = np.array([inputs]) + outputs = np.array([outputs]) + + plys = {} + limit = len(outputs[0]) + + for i in range(2, limit): + + model = sklearn.preprocessing.PolynomialFeatures(degree = i) + model = sklearn.pipeline.make_pipeline(model, sklearn.linear_model.LinearRegression()) + model = model.fit(np.rot90(inputs), np.rot90(outputs)) + + params = model.steps[1][1].intercept_.tolist() + params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::]) + params = params.flatten().tolist() + + temp = "" + counter = 0 + for param in params: + temp += "(" + str(param) + "*x^" + str(counter) + ")" + counter += 1 + plys["x^" + str(i)] = (temp) + + regressions["ply"] = (plys) + + if 'sig' in args: # formula: a tanh (b(x + c)) + d + + try: + + def sig(x, a, b, c, d): + + return a * np.tanh(b*(x + c)) + d + + popt, pcov = scipy.optimize.curve_fit(sig, X, y) + + coeffs = popt.flatten().tolist() + regressions["sig"] = (str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3])) + + except Exception as e: + + pass + + return regressions + +class Metric: + + def elo(self, starting_score, opposing_score, observed, N, K): + + return Elo.calculate(starting_score, opposing_score, observed, N, K) + + def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations): + + player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol) + + player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations) + + return (player.rating, player.rd, player.vol) + + def trueskill(self, teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]] + + team_ratings = [] + + for team in teams_data: + team_temp = () + for player in team: + player = Trueskill.Rating(player[0], player[1]) + team_temp = team_temp + (player,) + team_ratings.append(team_temp) + + return Trueskill.rate(team_ratings, ranks=observations) + +@jit(nopython=True) +def mean(data): + + return np.mean(data) + +@jit(nopython=True) +def median(data): + + return np.median(data) + +@jit(nopython=True) +def stdev(data): + + return np.std(data) + +@jit(nopython=True) +def variance(data): + + return np.var(data) + +@jit(nopython=True) +def npmin(data): + + return np.amin(data) + +@jit(nopython=True) +def npmax(data): + + return np.amax(data) + +@jit(forceobj=True) +def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"): + + kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm) + kernel.fit(data) + predictions = kernel.predict(data) + centers = kernel.cluster_centers_ + + return centers, predictions + +@jit(forceobj=True) +def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None): + + kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state) + + return kernel.fit_transform(data) + +@jit(forceobj=True) +def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth) + model = model.fit(data_train,labels_train) + predictions = model.predict(data_test) + metrics = ClassificationMetric(predictions, labels_test) + + return model, metrics \ No newline at end of file diff --git a/analysis-master/tra_analysis/Array.py b/analysis-master/tra_analysis/Array.py new file mode 100644 index 00000000..ab02c9a0 --- /dev/null +++ b/analysis-master/tra_analysis/Array.py @@ -0,0 +1,170 @@ +# Titan Robotics Team 2022: Array submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import Array' +# setup: + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - ported analysis.Array() here +""" + +__author__ = ( + "Arthur Lu ", +) + +import numpy as np + +class Array(): # tests on nd arrays independent of basic_stats + + def __init__(self, narray): + + self.array = np.array(narray) + + def __str__(self): + + return str(self.array) + + def elementwise_mean(self, *args, axis = 0): # expects arrays that are size normalized + if len(*args) == 0: + return np.mean(self.array, axis = axis) + else: + return np.mean([*args], axis = axis) + + def elementwise_median(self, *args, axis = 0): + + if len(*args) == 0: + return np.median(self.array, axis = axis) + else: + return np.median([*args], axis = axis) + + def elementwise_stdev(self, *args, axis = 0): + + if len(*args) == 0: + return np.std(self.array, axis = axis) + else: + return np.std([*args], axis = axis) + + def elementwise_variance(self, *args, axis = 0): + + if len(*args) == 0: + return np.var(self.array, axis = axis) + else: + return np.var([*args], axis = axis) + + def elementwise_npmin(self, *args, axis = 0): + + if len(*args) == 0: + return np.amin(self.array, axis = axis) + else: + return np.amin([*args], axis = axis) + + def elementwise_npmax(self, *args, axis = 0): + + if len(*args) == 0: + return np.amax(self.array, axis = axis) + else: + return np.amax([*args], axis = axis) + + def elementwise_stats(self, *args, axis = 0): + + _mean = self.elementwise_mean(*args, axis = axis) + _median = self.elementwise_median(*args, axis = axis) + _stdev = self.elementwise_stdev(*args, axis = axis) + _variance = self.elementwise_variance(*args, axis = axis) + _min = self.elementwise_npmin(*args, axis = axis) + _max = self.elementwise_npmax(*args, axis = axis) + + return _mean, _median, _stdev, _variance, _min, _max + + def __getitem__(self, key): + + return self.array[key] + + def __setitem__(self, key, value): + + self.array[key] == value + + def normalize(self, array): + + a = np.atleast_1d(np.linalg.norm(array)) + a[a==0] = 1 + return array / np.expand_dims(a, -1) + + def __add__(self, other): + + return self.array + other.array + + def __sub__(self, other): + + return self.array - other.array + + def __neg__(self): + + return -self.array + + def __abs__(self): + + return abs(self.array) + + def __invert__(self): + + return 1/self.array + + def __mul__(self, other): + + return self.array.dot(other.array) + + def __rmul__(self, other): + + return self.array.dot(other.array) + + def cross(self, other): + + return np.cross(self.array, other.array) + + def sort(self, array): # depreciated + warnings.warn("Array.sort has been depreciated in favor of Sort") + array_length = len(array) + if array_length <= 1: + return array + middle_index = int(array_length / 2) + left = array[0:middle_index] + right = array[middle_index:] + left = self.sort(left) + right = self.sort(right) + return self.__merge(left, right) + + + def __merge(self, left, right): + sorted_list = [] + left = left[:] + right = right[:] + while len(left) > 0 or len(right) > 0: + if len(left) > 0 and len(right) > 0: + if left[0] <= right[0]: + sorted_list.append(left.pop(0)) + else: + sorted_list.append(right.pop(0)) + elif len(left) > 0: + sorted_list.append(left.pop(0)) + elif len(right) > 0: + sorted_list.append(right.pop(0)) + return sorted_list + + def search(self, arr, x): + return self.__search(arr, 0, len(arr) - 1, x) + + def __search(self, arr, low, high, x): + if high >= low: + mid = (high + low) // 2 + if arr[mid] == x: + return mid + elif arr[mid] > x: + return binary_search(arr, low, mid - 1, x) + else: + return binary_search(arr, mid + 1, high, x) + else: + return -1 \ No newline at end of file diff --git a/analysis-master/tra_analysis/ClassificationMetric.py b/analysis-master/tra_analysis/ClassificationMetric.py new file mode 100644 index 00000000..6135aeda --- /dev/null +++ b/analysis-master/tra_analysis/ClassificationMetric.py @@ -0,0 +1,36 @@ +# Titan Robotics Team 2022: ClassificationMetric submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import ClassificationMetric' +# setup: + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - ported analysis.ClassificationMetric() here +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ +] + +import sklearn +from sklearn import metrics + +class ClassificationMetric(): + + def __new__(cls, predictions, targets): + + return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets) + + def cm(self, predictions, targets): + + return sklearn.metrics.confusion_matrix(targets, predictions) + + def cr(self, predictions, targets): + + return sklearn.metrics.classification_report(targets, predictions) \ No newline at end of file diff --git a/analysis-master/tra_analysis/CorrelationTest.py b/analysis-master/tra_analysis/CorrelationTest.py new file mode 100644 index 00000000..1fd841ef --- /dev/null +++ b/analysis-master/tra_analysis/CorrelationTest.py @@ -0,0 +1,58 @@ +# Titan Robotics Team 2022: CorrelationTest submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import CorrelationTest' +# setup: + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - ported analysis.CorrelationTest() here + - removed classness +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ +] + +import scipy +from scipy import stats + +def anova_oneway(self, *args): #expects arrays of samples + + results = scipy.stats.f_oneway(*args) + return {"f-value": results[0], "p-value": results[1]} + +def pearson(self, x, y): + + results = scipy.stats.pearsonr(x, y) + return {"r-value": results[0], "p-value": results[1]} + +def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'): + + results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy) + return {"r-value": results[0], "p-value": results[1]} + +def point_biserial(self, x,y): + + results = scipy.stats.pointbiserialr(x, y) + return {"r-value": results[0], "p-value": results[1]} + +def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'): + + results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method) + return {"tau": results[0], "p-value": results[1]} + +def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True): + + results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive) + return {"tau": results[0], "p-value": results[1]} + +def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None): + + results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state) + return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value \ No newline at end of file diff --git a/analysis-master/tra_analysis/CorrelationTest_obj.py b/analysis-master/tra_analysis/CorrelationTest_obj.py new file mode 100644 index 00000000..16168094 --- /dev/null +++ b/analysis-master/tra_analysis/CorrelationTest_obj.py @@ -0,0 +1,41 @@ +# Only included for backwards compatibility! Do not update, CorrelationTest is preferred and supported. + +import scipy +from scipy import stats + +class CorrelationTest: + + def anova_oneway(self, *args): #expects arrays of samples + + results = scipy.stats.f_oneway(*args) + return {"f-value": results[0], "p-value": results[1]} + + def pearson(self, x, y): + + results = scipy.stats.pearsonr(x, y) + return {"r-value": results[0], "p-value": results[1]} + + def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'): + + results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy) + return {"r-value": results[0], "p-value": results[1]} + + def point_biserial(self, x,y): + + results = scipy.stats.pointbiserialr(x, y) + return {"r-value": results[0], "p-value": results[1]} + + def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'): + + results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method) + return {"tau": results[0], "p-value": results[1]} + + def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True): + + results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive) + return {"tau": results[0], "p-value": results[1]} + + def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None): + + results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state) + return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value \ No newline at end of file diff --git a/analysis-master/tra_analysis/fits.py b/analysis-master/tra_analysis/Fit.py similarity index 96% rename from analysis-master/tra_analysis/fits.py rename to analysis-master/tra_analysis/Fit.py index 00935cb3..9780331a 100644 --- a/analysis-master/tra_analysis/fits.py +++ b/analysis-master/tra_analysis/Fit.py @@ -4,10 +4,12 @@ # this module is cuda-optimized (as appropriate) and vectorized (except for one small part) # setup: -__version__ = "0.0.1" +__version__ = "0.0.2" # changelog should be viewed using print(analysis.fits.__changelog__) __changelog__ = """changelog: + 0.0.2: + - renamed module to Fit 0.0.1: - initial release, add circle fitting with LSC """ diff --git a/analysis-master/tra_analysis/KNN.py b/analysis-master/tra_analysis/KNN.py new file mode 100644 index 00000000..ed9ca9df --- /dev/null +++ b/analysis-master/tra_analysis/KNN.py @@ -0,0 +1,42 @@ +# Titan Robotics Team 2022: KNN submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import KNN' +# setup: + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - ported analysis.KNN() here + - removed classness +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ +] + +import sklearn +from sklearn import model_selection, neighbors +from . import ClassificationMetric, RegressionMetric + +def knn_classifier(self, data, labels, n_neighbors, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.neighbors.KNeighborsClassifier() + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetric(predictions, labels_test) + +def knn_regressor(self, data, outputs, n_neighbors, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None): + + data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) + model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs) + model.fit(data_train, outputs_train) + predictions = model.predict(data_test) + + return model, RegressionMetric(predictions, outputs_test) \ No newline at end of file diff --git a/analysis-master/tra_analysis/KNN_obj.py b/analysis-master/tra_analysis/KNN_obj.py new file mode 100644 index 00000000..b506bf6c --- /dev/null +++ b/analysis-master/tra_analysis/KNN_obj.py @@ -0,0 +1,25 @@ +# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported. + +import sklearn +from sklearn import model_selection, neighbors +from . import ClassificationMetric, RegressionMetric + +class KNN: + + def knn_classifier(self, data, labels, n_neighbors, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.neighbors.KNeighborsClassifier() + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetric(predictions, labels_test) + + def knn_regressor(self, data, outputs, n_neighbors, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None): + + data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) + model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs) + model.fit(data_train, outputs_train) + predictions = model.predict(data_test) + + return model, RegressionMetric(predictions, outputs_test) \ No newline at end of file diff --git a/analysis-master/tra_analysis/NaiveBayes.py b/analysis-master/tra_analysis/NaiveBayes.py new file mode 100644 index 00000000..c666975a --- /dev/null +++ b/analysis-master/tra_analysis/NaiveBayes.py @@ -0,0 +1,59 @@ +# Titan Robotics Team 2022: NaiveBayes submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import NaiveBayes' +# setup: + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - ported analysis.NaiveBayes() here +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ +] + +import sklearn +from sklearn import model_selection, naive_bayes +from . import ClassificationMetric, RegressionMetric + +def guassian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetric(predictions, labels_test) + +def multinomial(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetric(predictions, labels_test) + +def bernoulli(data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetric(predictions, labels_test) + +def complement(data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetric(predictions, labels_test) \ No newline at end of file diff --git a/analysis-master/tra_analysis/NaiveBayes_obj.py b/analysis-master/tra_analysis/NaiveBayes_obj.py new file mode 100644 index 00000000..70e4d437 --- /dev/null +++ b/analysis-master/tra_analysis/NaiveBayes_obj.py @@ -0,0 +1,43 @@ +# Only included for backwards compatibility! Do not update, NaiveBayes is preferred and supported. + +import sklearn +from sklearn import model_selection, naive_bayes +from . import ClassificationMetric, RegressionMetric + +class NaiveBayes: + + def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetric(predictions, labels_test) + + def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetric(predictions, labels_test) + + def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetric(predictions, labels_test) + + def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm) + model.fit(data_train, labels_train) + predictions = model.predict(data_test) + + return model, ClassificationMetric(predictions, labels_test) \ No newline at end of file diff --git a/analysis-master/tra_analysis/RandomForest.py b/analysis-master/tra_analysis/RandomForest.py new file mode 100644 index 00000000..b39b56f7 --- /dev/null +++ b/analysis-master/tra_analysis/RandomForest.py @@ -0,0 +1,42 @@ +# Titan Robotics Team 2022: RandomForest submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import RandomForest' +# setup: + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - ported analysis.RandomFores() here + - removed classness +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ +] + +import sklearn +from sklearn import ensemble, model_selection +from . import ClassificationMetric, RegressionMetric + +def random_forest_classifier(data, labels, test_size, n_estimators, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight) + kernel.fit(data_train, labels_train) + predictions = kernel.predict(data_test) + + return kernel, ClassificationMetric(predictions, labels_test) + +def random_forest_regressor(data, outputs, test_size, n_estimators, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False): + + data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) + kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start) + kernel.fit(data_train, outputs_train) + predictions = kernel.predict(data_test) + + return kernel, RegressionMetric(predictions, outputs_test) \ No newline at end of file diff --git a/analysis-master/tra_analysis/RandomForest_obj.py b/analysis-master/tra_analysis/RandomForest_obj.py new file mode 100644 index 00000000..c33f8dce --- /dev/null +++ b/analysis-master/tra_analysis/RandomForest_obj.py @@ -0,0 +1,25 @@ +# Only included for backwards compatibility! Do not update, RandomForest is preferred and supported. + +import sklearn +from sklearn import ensemble, model_selection +from . import ClassificationMetric, RegressionMetric + +class RandomForest: + + def random_forest_classifier(self, data, labels, test_size, n_estimators, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None): + + data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) + kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight) + kernel.fit(data_train, labels_train) + predictions = kernel.predict(data_test) + + return kernel, ClassificationMetric(predictions, labels_test) + + def random_forest_regressor(self, data, outputs, test_size, n_estimators, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False): + + data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) + kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start) + kernel.fit(data_train, outputs_train) + predictions = kernel.predict(data_test) + + return kernel, RegressionMetric(predictions, outputs_test) \ No newline at end of file diff --git a/analysis-master/tra_analysis/RegressionMetric.py b/analysis-master/tra_analysis/RegressionMetric.py new file mode 100644 index 00000000..37904a04 --- /dev/null +++ b/analysis-master/tra_analysis/RegressionMetric.py @@ -0,0 +1,40 @@ +# Titan Robotics Team 2022: RegressionMetric submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import RegressionMetric' +# setup: + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - ported analysis.RegressionMetric() here +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ +] + +import sklearn +from sklearn import metrics + +class RegressionMetric(): + + def __new__(cls, predictions, targets): + + return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets) + + def r_squared(self, predictions, targets): # assumes equal size inputs + + return sklearn.metrics.r2_score(targets, predictions) + + def mse(self, predictions, targets): + + return sklearn.metrics.mean_squared_error(targets, predictions) + + def rms(self, predictions, targets): + + return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions)) \ No newline at end of file diff --git a/analysis-master/tra_analysis/SVM.py b/analysis-master/tra_analysis/SVM.py new file mode 100644 index 00000000..ef568c5f --- /dev/null +++ b/analysis-master/tra_analysis/SVM.py @@ -0,0 +1,79 @@ +# Titan Robotics Team 2022: SVM submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import SVM' +# setup: + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - ported analysis.SVM() here +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ +] + +import sklearn +from sklearn import svm +from . import ClassificationMetric, RegressionMetric + +class SVM: + + class CustomKernel: + + def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state): + + return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) + + class StandardKernel: + + def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None): + + return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) + + class PrebuiltKernel: + + class Linear: + + def __new__(cls): + + return sklearn.svm.SVC(kernel = 'linear') + + class Polynomial: + + def __new__(cls, power, r_bias): + + return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias) + + class RBF: + + def __new__(cls, gamma): + + return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma) + + class Sigmoid: + + def __new__(cls, r_bias): + + return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias) + + def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs + + return kernel.fit(train_data, train_outputs) + + def eval_classification(self, kernel, test_data, test_outputs): + + predictions = kernel.predict(test_data) + + return ClassificationMetric(predictions, test_outputs) + + def eval_regression(self, kernel, test_data, test_outputs): + + predictions = kernel.predict(test_data) + + return RegressionMetric(predictions, test_outputs) \ No newline at end of file diff --git a/analysis-master/tra_analysis/Sort.py b/analysis-master/tra_analysis/Sort.py new file mode 100644 index 00000000..b6a446ee --- /dev/null +++ b/analysis-master/tra_analysis/Sort.py @@ -0,0 +1,408 @@ +# Titan Robotics Team 2022: Sort submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import Sort' +# setup: + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - ported analysis.Sort() here + - removed classness +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ +] + +def quicksort(a): + + def sort(array): + + less = [] + equal = [] + greater = [] + + if len(array) > 1: + pivot = array[0] + for x in array: + if x < pivot: + less.append(x) + elif x == pivot: + equal.append(x) + elif x > pivot: + greater.append(x) + return sort(less)+equal+sort(greater) + else: + return array + + return np.array(sort(a)) + +def mergesort(a): + + def sort(array): + + array = array + + if len(array) >1: + middle = len(array) // 2 + L = array[:middle] + R = array[middle:] + + sort(L) + sort(R) + + i = j = k = 0 + + while i < len(L) and j < len(R): + if L[i] < R[j]: + array[k] = L[i] + i+= 1 + else: + array[k] = R[j] + j+= 1 + k+= 1 + + while i < len(L): + array[k] = L[i] + i+= 1 + k+= 1 + + while j < len(R): + array[k] = R[j] + j+= 1 + k+= 1 + + return array + + return sort(a) + +def introsort(a): + + def sort(array, start, end, maxdepth): + + array = array + + if end - start <= 1: + return + elif maxdepth == 0: + heapsort(array, start, end) + else: + p = partition(array, start, end) + sort(array, start, p + 1, maxdepth - 1) + sort(array, p + 1, end, maxdepth - 1) + + return array + + def partition(array, start, end): + pivot = array[start] + i = start - 1 + j = end + + while True: + i = i + 1 + while array[i] < pivot: + i = i + 1 + j = j - 1 + while array[j] > pivot: + j = j - 1 + + if i >= j: + return j + + swap(array, i, j) + + def swap(array, i, j): + array[i], array[j] = array[j], array[i] + + def heapsort(array, start, end): + build_max_heap(array, start, end) + for i in range(end - 1, start, -1): + swap(array, start, i) + max_heapify(array, index=0, start=start, end=i) + + def build_max_heap(array, start, end): + def parent(i): + return (i - 1)//2 + length = end - start + index = parent(length - 1) + while index >= 0: + max_heapify(array, index, start, end) + index = index - 1 + + def max_heapify(array, index, start, end): + def left(i): + return 2*i + 1 + def right(i): + return 2*i + 2 + + size = end - start + l = left(index) + r = right(index) + if (l < size and array[start + l] > array[start + index]): + largest = l + else: + largest = index + if (r < size and array[start + r] > array[start + largest]): + largest = r + if largest != index: + swap(array, start + largest, start + index) + max_heapify(array, largest, start, end) + + maxdepth = (len(a).bit_length() - 1)*2 + + return sort(a, 0, len(a), maxdepth) + +def heapsort(a): + + def sort(array): + + array = array + + n = len(array) + + for i in range(n//2 - 1, -1, -1): + heapify(array, n, i) + + for i in range(n-1, 0, -1): + array[i], array[0] = array[0], array[i] + heapify(array, i, 0) + + return array + + def heapify(array, n, i): + + array = array + + largest = i + l = 2 * i + 1 + r = 2 * i + 2 + + if l < n and array[i] < array[l]: + largest = l + + if r < n and array[largest] < array[r]: + largest = r + + if largest != i: + array[i],array[largest] = array[largest],array[i] + heapify(array, n, largest) + + return array + + return sort(a) + +def insertionsort(a): + + def sort(array): + + array = array + + for i in range(1, len(array)): + + key = array[i] + + j = i-1 + while j >=0 and key < array[j] : + array[j+1] = array[j] + j -= 1 + array[j+1] = key + + return array + + return sort(a) + +def timsort(a, block = 32): + + BLOCK = block + + def sort(array, n): + + array = array + + for i in range(0, n, BLOCK): + insertionsort(array, i, min((i+31), (n-1))) + + size = BLOCK + while size < n: + + for left in range(0, n, 2*size): + + mid = left + size - 1 + right = min((left + 2*size - 1), (n-1)) + merge(array, left, mid, right) + + size = 2*size + + return array + + def insertionsort(array, left, right): + + array = array + + for i in range(left + 1, right+1): + + temp = array[i] + j = i - 1 + while j >= left and array[j] > temp : + + array[j+1] = array[j] + j -= 1 + + array[j+1] = temp + + return array + + + def merge(array, l, m, r): + + len1, len2 = m - l + 1, r - m + left, right = [], [] + for i in range(0, len1): + left.append(array[l + i]) + for i in range(0, len2): + right.append(array[m + 1 + i]) + + i, j, k = 0, 0, l + + while i < len1 and j < len2: + + if left[i] <= right[j]: + array[k] = left[i] + i += 1 + + else: + array[k] = right[j] + j += 1 + + k += 1 + + while i < len1: + + array[k] = left[i] + k += 1 + i += 1 + + while j < len2: + array[k] = right[j] + k += 1 + j += 1 + + return sort(a, len(a)) + +def selectionsort(a): + array = a + for i in range(len(array)): + min_idx = i + for j in range(i+1, len(array)): + if array[min_idx] > array[j]: + min_idx = j + array[i], array[min_idx] = array[min_idx], array[i] + return array + +def shellsort(a): + array = a + n = len(array) + gap = n//2 + + while gap > 0: + + for i in range(gap,n): + + temp = array[i] + j = i + while j >= gap and array[j-gap] >temp: + array[j] = array[j-gap] + j -= gap + array[j] = temp + gap //= 2 + + return array + +def bubblesort(a): + + def sort(array): + for i, num in enumerate(array): + try: + if array[i+1] < num: + array[i] = array[i+1] + array[i+1] = num + sort(array) + except IndexError: + pass + return array + + return sort(a) + +def cyclesort(a): + + def sort(array): + + array = array + writes = 0 + + for cycleStart in range(0, len(array) - 1): + item = array[cycleStart] + + pos = cycleStart + for i in range(cycleStart + 1, len(array)): + if array[i] < item: + pos += 1 + + if pos == cycleStart: + continue + + while item == array[pos]: + pos += 1 + array[pos], item = item, array[pos] + writes += 1 + + while pos != cycleStart: + + pos = cycleStart + for i in range(cycleStart + 1, len(array)): + if array[i] < item: + pos += 1 + + while item == array[pos]: + pos += 1 + array[pos], item = item, array[pos] + writes += 1 + + return array + + return sort(a) + +def cocktailsort(a): + + def sort(array): + + array = array + + n = len(array) + swapped = True + start = 0 + end = n-1 + while (swapped == True): + swapped = False + for i in range (start, end): + if (array[i] > array[i + 1]) : + array[i], array[i + 1]= array[i + 1], array[i] + swapped = True + if (swapped == False): + break + swapped = False + end = end-1 + for i in range(end-1, start-1, -1): + if (array[i] > array[i + 1]): + array[i], array[i + 1] = array[i + 1], array[i] + swapped = True + start = start + 1 + + return array + + return sort(a) \ No newline at end of file diff --git a/analysis-master/tra_analysis/Sort_obj.py b/analysis-master/tra_analysis/Sort_obj.py new file mode 100644 index 00000000..b80b4760 --- /dev/null +++ b/analysis-master/tra_analysis/Sort_obj.py @@ -0,0 +1,391 @@ +# Only included for backwards compatibility! Do not update, Sort is preferred and supported. + +class Sort: # if you haven't used a sort, then you've never lived + + def quicksort(self, a): + + def sort(array): + + less = [] + equal = [] + greater = [] + + if len(array) > 1: + pivot = array[0] + for x in array: + if x < pivot: + less.append(x) + elif x == pivot: + equal.append(x) + elif x > pivot: + greater.append(x) + return sort(less)+equal+sort(greater) + else: + return array + + return np.array(sort(a)) + + def mergesort(self, a): + + def sort(array): + + array = array + + if len(array) >1: + middle = len(array) // 2 + L = array[:middle] + R = array[middle:] + + sort(L) + sort(R) + + i = j = k = 0 + + while i < len(L) and j < len(R): + if L[i] < R[j]: + array[k] = L[i] + i+= 1 + else: + array[k] = R[j] + j+= 1 + k+= 1 + + while i < len(L): + array[k] = L[i] + i+= 1 + k+= 1 + + while j < len(R): + array[k] = R[j] + j+= 1 + k+= 1 + + return array + + return sort(a) + + def introsort(self, a): + + def sort(array, start, end, maxdepth): + + array = array + + if end - start <= 1: + return + elif maxdepth == 0: + heapsort(array, start, end) + else: + p = partition(array, start, end) + sort(array, start, p + 1, maxdepth - 1) + sort(array, p + 1, end, maxdepth - 1) + + return array + + def partition(array, start, end): + pivot = array[start] + i = start - 1 + j = end + + while True: + i = i + 1 + while array[i] < pivot: + i = i + 1 + j = j - 1 + while array[j] > pivot: + j = j - 1 + + if i >= j: + return j + + swap(array, i, j) + + def swap(array, i, j): + array[i], array[j] = array[j], array[i] + + def heapsort(array, start, end): + build_max_heap(array, start, end) + for i in range(end - 1, start, -1): + swap(array, start, i) + max_heapify(array, index=0, start=start, end=i) + + def build_max_heap(array, start, end): + def parent(i): + return (i - 1)//2 + length = end - start + index = parent(length - 1) + while index >= 0: + max_heapify(array, index, start, end) + index = index - 1 + + def max_heapify(array, index, start, end): + def left(i): + return 2*i + 1 + def right(i): + return 2*i + 2 + + size = end - start + l = left(index) + r = right(index) + if (l < size and array[start + l] > array[start + index]): + largest = l + else: + largest = index + if (r < size and array[start + r] > array[start + largest]): + largest = r + if largest != index: + swap(array, start + largest, start + index) + max_heapify(array, largest, start, end) + + maxdepth = (len(a).bit_length() - 1)*2 + + return sort(a, 0, len(a), maxdepth) + + def heapsort(self, a): + + def sort(array): + + array = array + + n = len(array) + + for i in range(n//2 - 1, -1, -1): + heapify(array, n, i) + + for i in range(n-1, 0, -1): + array[i], array[0] = array[0], array[i] + heapify(array, i, 0) + + return array + + def heapify(array, n, i): + + array = array + + largest = i + l = 2 * i + 1 + r = 2 * i + 2 + + if l < n and array[i] < array[l]: + largest = l + + if r < n and array[largest] < array[r]: + largest = r + + if largest != i: + array[i],array[largest] = array[largest],array[i] + heapify(array, n, largest) + + return array + + return sort(a) + + def insertionsort(self, a): + + def sort(array): + + array = array + + for i in range(1, len(array)): + + key = array[i] + + j = i-1 + while j >=0 and key < array[j] : + array[j+1] = array[j] + j -= 1 + array[j+1] = key + + return array + + return sort(a) + + def timsort(self, a, block = 32): + + BLOCK = block + + def sort(array, n): + + array = array + + for i in range(0, n, BLOCK): + insertionsort(array, i, min((i+31), (n-1))) + + size = BLOCK + while size < n: + + for left in range(0, n, 2*size): + + mid = left + size - 1 + right = min((left + 2*size - 1), (n-1)) + merge(array, left, mid, right) + + size = 2*size + + return array + + def insertionsort(array, left, right): + + array = array + + for i in range(left + 1, right+1): + + temp = array[i] + j = i - 1 + while j >= left and array[j] > temp : + + array[j+1] = array[j] + j -= 1 + + array[j+1] = temp + + return array + + + def merge(array, l, m, r): + + len1, len2 = m - l + 1, r - m + left, right = [], [] + for i in range(0, len1): + left.append(array[l + i]) + for i in range(0, len2): + right.append(array[m + 1 + i]) + + i, j, k = 0, 0, l + + while i < len1 and j < len2: + + if left[i] <= right[j]: + array[k] = left[i] + i += 1 + + else: + array[k] = right[j] + j += 1 + + k += 1 + + while i < len1: + + array[k] = left[i] + k += 1 + i += 1 + + while j < len2: + array[k] = right[j] + k += 1 + j += 1 + + return sort(a, len(a)) + + def selectionsort(self, a): + array = a + for i in range(len(array)): + min_idx = i + for j in range(i+1, len(array)): + if array[min_idx] > array[j]: + min_idx = j + array[i], array[min_idx] = array[min_idx], array[i] + return array + + def shellsort(self, a): + array = a + n = len(array) + gap = n//2 + + while gap > 0: + + for i in range(gap,n): + + temp = array[i] + j = i + while j >= gap and array[j-gap] >temp: + array[j] = array[j-gap] + j -= gap + array[j] = temp + gap //= 2 + + return array + + def bubblesort(self, a): + + def sort(array): + for i, num in enumerate(array): + try: + if array[i+1] < num: + array[i] = array[i+1] + array[i+1] = num + sort(array) + except IndexError: + pass + return array + + return sort(a) + + def cyclesort(self, a): + + def sort(array): + + array = array + writes = 0 + + for cycleStart in range(0, len(array) - 1): + item = array[cycleStart] + + pos = cycleStart + for i in range(cycleStart + 1, len(array)): + if array[i] < item: + pos += 1 + + if pos == cycleStart: + continue + + while item == array[pos]: + pos += 1 + array[pos], item = item, array[pos] + writes += 1 + + while pos != cycleStart: + + pos = cycleStart + for i in range(cycleStart + 1, len(array)): + if array[i] < item: + pos += 1 + + while item == array[pos]: + pos += 1 + array[pos], item = item, array[pos] + writes += 1 + + return array + + return sort(a) + + def cocktailsort(self, a): + + def sort(array): + + array = array + + n = len(array) + swapped = True + start = 0 + end = n-1 + while (swapped == True): + swapped = False + for i in range (start, end): + if (array[i] > array[i + 1]) : + array[i], array[i + 1]= array[i + 1], array[i] + swapped = True + if (swapped == False): + break + swapped = False + end = end-1 + for i in range(end-1, start-1, -1): + if (array[i] > array[i + 1]): + array[i], array[i + 1] = array[i + 1], array[i] + swapped = True + start = start + 1 + + return array + + return sort(a) \ No newline at end of file diff --git a/analysis-master/tra_analysis/StatisticalTest.py b/analysis-master/tra_analysis/StatisticalTest.py new file mode 100644 index 00000000..8bc84fbe --- /dev/null +++ b/analysis-master/tra_analysis/StatisticalTest.py @@ -0,0 +1,187 @@ +# Titan Robotics Team 2022: StatisticalTest submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import StatisticalTest' +# setup: + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - ported analysis.StatisticalTest() here + - removed classness +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = [ +] + +import scipy +from scipy import stats + +def ttest_onesample(a, popmean, axis = 0, nan_policy = 'propagate'): + + results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy) + return {"t-value": results[0], "p-value": results[1]} + +def ttest_independent(a, b, equal = True, nan_policy = 'propagate'): + + results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy) + return {"t-value": results[0], "p-value": results[1]} + +def ttest_statistic(o1, o2, equal = True): + + results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal) + return {"t-value": results[0], "p-value": results[1]} + +def ttest_related(a, b, axis = 0, nan_policy='propagate'): + + results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy) + return {"t-value": results[0], "p-value": results[1]} + +def ks_fitness(rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'): + + results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode) + return {"ks-value": results[0], "p-value": results[1]} + +def chisquare(f_obs, f_exp = None, ddof = None, axis = 0): + + results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis) + return {"chisquared-value": results[0], "p-value": results[1]} + +def powerdivergence(f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None): + + results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_) + return {"powerdivergence-value": results[0], "p-value": results[1]} + +def ks_twosample(x, y, alternative = 'two_sided', mode = 'auto'): + + results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode) + return {"ks-value": results[0], "p-value": results[1]} + +def es_twosample(x, y, t = (0.4, 0.8)): + + results = scipy.stats.epps_singleton_2samp(x, y, t = t) + return {"es-value": results[0], "p-value": results[1]} + +def mw_rank(x, y, use_continuity = True, alternative = None): + + results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative) + return {"u-value": results[0], "p-value": results[1]} + +def mw_tiecorrection(rank_values): + + results = scipy.stats.tiecorrect(rank_values) + return {"correction-factor": results} + +def rankdata(a, method = 'average'): + + results = scipy.stats.rankdata(a, method = method) + return results + +def wilcoxon_ranksum(a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test + + results = scipy.stats.ranksums(a, b) + return {"u-value": results[0], "p-value": results[1]} + +def wilcoxon_signedrank(x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'): + + results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative) + return {"t-value": results[0], "p-value": results[1]} + +def kw_htest(*args, nan_policy = 'propagate'): + + results = scipy.stats.kruskal(*args, nan_policy = nan_policy) + return {"h-value": results[0], "p-value": results[1]} + +def friedman_chisquare(*args): + + results = scipy.stats.friedmanchisquare(*args) + return {"chisquared-value": results[0], "p-value": results[1]} + +def bm_wtest(x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'): + + results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy) + return {"w-value": results[0], "p-value": results[1]} + +def combine_pvalues(pvalues, method = 'fisher', weights = None): + + results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights) + return {"combined-statistic": results[0], "p-value": results[1]} + +def jb_fitness(x): + + results = scipy.stats.jarque_bera(x) + return {"jb-value": results[0], "p-value": results[1]} + +def ab_equality(x, y): + + results = scipy.stats.ansari(x, y) + return {"ab-value": results[0], "p-value": results[1]} + +def bartlett_variance(*args): + + results = scipy.stats.bartlett(*args) + return {"t-value": results[0], "p-value": results[1]} + +def levene_variance(*args, center = 'median', proportiontocut = 0.05): + + results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut) + return {"w-value": results[0], "p-value": results[1]} + +def sw_normality(x): + + results = scipy.stats.shapiro(x) + return {"w-value": results[0], "p-value": results[1]} + +def shapiro(x): + + return "destroyed by facts and logic" + +def ad_onesample(x, dist = 'norm'): + + results = scipy.stats.anderson(x, dist = dist) + return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]} + +def ad_ksample(samples, midrank = True): + + results = scipy.stats.anderson_ksamp(samples, midrank = midrank) + return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]} + +def binomial(x, n = None, p = 0.5, alternative = 'two-sided'): + + results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative) + return {"p-value": results} + +def fk_variance(*args, center = 'median', proportiontocut = 0.05): + + results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut) + return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value + +def mood_mediantest(*args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'): + + results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy) + return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]} + +def mood_equalscale(x, y, axis = 0): + + results = scipy.stats.mood(x, y, axis = axis) + return {"z-score": results[0], "p-value": results[1]} + +def skewtest(a, axis = 0, nan_policy = 'propogate'): + + results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy) + return {"z-score": results[0], "p-value": results[1]} + +def kurtosistest(a, axis = 0, nan_policy = 'propogate'): + + results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy) + return {"z-score": results[0], "p-value": results[1]} + +def normaltest(a, axis = 0, nan_policy = 'propogate'): + + results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy) + return {"z-score": results[0], "p-value": results[1]} \ No newline at end of file diff --git a/analysis-master/tra_analysis/StatisticalTest_obj.py b/analysis-master/tra_analysis/StatisticalTest_obj.py new file mode 100644 index 00000000..b45c2f9f --- /dev/null +++ b/analysis-master/tra_analysis/StatisticalTest_obj.py @@ -0,0 +1,170 @@ +# Only included for backwards compatibility! Do not update, StatisticalTest is preferred and supported. + +import scipy +from scipy import stats + +class StatisticalTest: + + def ttest_onesample(self, a, popmean, axis = 0, nan_policy = 'propagate'): + + results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy) + return {"t-value": results[0], "p-value": results[1]} + + def ttest_independent(self, a, b, equal = True, nan_policy = 'propagate'): + + results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy) + return {"t-value": results[0], "p-value": results[1]} + + def ttest_statistic(self, o1, o2, equal = True): + + results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal) + return {"t-value": results[0], "p-value": results[1]} + + def ttest_related(self, a, b, axis = 0, nan_policy='propagate'): + + results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy) + return {"t-value": results[0], "p-value": results[1]} + + def ks_fitness(self, rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'): + + results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode) + return {"ks-value": results[0], "p-value": results[1]} + + def chisquare(self, f_obs, f_exp = None, ddof = None, axis = 0): + + results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis) + return {"chisquared-value": results[0], "p-value": results[1]} + + def powerdivergence(self, f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None): + + results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_) + return {"powerdivergence-value": results[0], "p-value": results[1]} + + def ks_twosample(self, x, y, alternative = 'two_sided', mode = 'auto'): + + results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode) + return {"ks-value": results[0], "p-value": results[1]} + + def es_twosample(self, x, y, t = (0.4, 0.8)): + + results = scipy.stats.epps_singleton_2samp(x, y, t = t) + return {"es-value": results[0], "p-value": results[1]} + + def mw_rank(self, x, y, use_continuity = True, alternative = None): + + results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative) + return {"u-value": results[0], "p-value": results[1]} + + def mw_tiecorrection(self, rank_values): + + results = scipy.stats.tiecorrect(rank_values) + return {"correction-factor": results} + + def rankdata(self, a, method = 'average'): + + results = scipy.stats.rankdata(a, method = method) + return results + + def wilcoxon_ranksum(self, a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test + + results = scipy.stats.ranksums(a, b) + return {"u-value": results[0], "p-value": results[1]} + + def wilcoxon_signedrank(self, x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'): + + results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative) + return {"t-value": results[0], "p-value": results[1]} + + def kw_htest(self, *args, nan_policy = 'propagate'): + + results = scipy.stats.kruskal(*args, nan_policy = nan_policy) + return {"h-value": results[0], "p-value": results[1]} + + def friedman_chisquare(self, *args): + + results = scipy.stats.friedmanchisquare(*args) + return {"chisquared-value": results[0], "p-value": results[1]} + + def bm_wtest(self, x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'): + + results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy) + return {"w-value": results[0], "p-value": results[1]} + + def combine_pvalues(self, pvalues, method = 'fisher', weights = None): + + results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights) + return {"combined-statistic": results[0], "p-value": results[1]} + + def jb_fitness(self, x): + + results = scipy.stats.jarque_bera(x) + return {"jb-value": results[0], "p-value": results[1]} + + def ab_equality(self, x, y): + + results = scipy.stats.ansari(x, y) + return {"ab-value": results[0], "p-value": results[1]} + + def bartlett_variance(self, *args): + + results = scipy.stats.bartlett(*args) + return {"t-value": results[0], "p-value": results[1]} + + def levene_variance(self, *args, center = 'median', proportiontocut = 0.05): + + results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut) + return {"w-value": results[0], "p-value": results[1]} + + def sw_normality(self, x): + + results = scipy.stats.shapiro(x) + return {"w-value": results[0], "p-value": results[1]} + + def shapiro(self, x): + + return "destroyed by facts and logic" + + def ad_onesample(self, x, dist = 'norm'): + + results = scipy.stats.anderson(x, dist = dist) + return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]} + + def ad_ksample(self, samples, midrank = True): + + results = scipy.stats.anderson_ksamp(samples, midrank = midrank) + return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]} + + def binomial(self, x, n = None, p = 0.5, alternative = 'two-sided'): + + results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative) + return {"p-value": results} + + def fk_variance(self, *args, center = 'median', proportiontocut = 0.05): + + results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut) + return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value + + def mood_mediantest(self, *args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'): + + results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy) + return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]} + + def mood_equalscale(self, x, y, axis = 0): + + results = scipy.stats.mood(x, y, axis = axis) + return {"z-score": results[0], "p-value": results[1]} + + def skewtest(self, a, axis = 0, nan_policy = 'propogate'): + + results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy) + return {"z-score": results[0], "p-value": results[1]} + + def kurtosistest(self, a, axis = 0, nan_policy = 'propogate'): + + results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy) + return {"z-score": results[0], "p-value": results[1]} + + def normaltest(self, a, axis = 0, nan_policy = 'propogate'): + + results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy) + return {"z-score": results[0], "p-value": results[1]} \ No newline at end of file diff --git a/analysis-master/tra_analysis/__init__.py b/analysis-master/tra_analysis/__init__.py index e69de29b..6ea9c9b7 100644 --- a/analysis-master/tra_analysis/__init__.py +++ b/analysis-master/tra_analysis/__init__.py @@ -0,0 +1,39 @@ +# Titan Robotics Team 2022: tra_analysis package +# Written by Arthur Lu, Jacob Levine, and Dev Singh +# Notes: +# this should be imported as a python package using 'import tra_analysis' +# this should be included in the local directory or environment variable +# this module has been optimized for multhreaded computing +# current benchmark of optimization: 1.33 times faster +# setup: + +__version__ = "2.1.0-alpha.1" + +# changelog should be viewed using print(analysis.__changelog__) +__changelog__ = """changelog: + 2.1.0-alpha.1: + - moved multiple submodules under analysis to their own modules/files + - added header, __version__, __changelog__, __author__, __all__ (unpopulated) +""" + +__author__ = ( + "Arthur Lu ", + "Jacob Levine ", + "Dev Singh ", +) + +__all__ = [ +] + +from . import Analysis +from .Array import Array +from .ClassificationMetric import ClassificationMetric +from . import CorrelationTest +from . import Fit +from . import KNN +from . import NaiveBayes +from . import RandomForest +from .RegressionMetric import RegressionMetric +from . import Sort +from . import StatisticalTest +from .SVM import SVM \ No newline at end of file diff --git a/analysis-master/tra_analysis/analysis.py b/analysis-master/tra_analysis/analysis.py deleted file mode 100644 index 14de855a..00000000 --- a/analysis-master/tra_analysis/analysis.py +++ /dev/null @@ -1,1531 +0,0 @@ -# Titan Robotics Team 2022: Data Analysis Module -# Written by Arthur Lu, Jacob Levine, and Dev Singh -# Notes: -# this should be imported as a python module using 'from tra_analysis import analysis' -# this should be included in the local directory or environment variable -# this module has been optimized for multhreaded computing -# current benchmark of optimization: 1.33 times faster -# setup: - -__version__ = "2.3.1" - -# changelog should be viewed using print(analysis.__changelog__) -__changelog__ = """changelog: - 2.3.1: - - fixed bugs in Array class - 2.3.0: - - overhauled Array class - 2.2.3: - - fixed spelling of RandomForest - - made n_neighbors required for KNN - - made n_classifiers required for SVM - 2.2.2: - - fixed 2.2.1 changelog entry - - changed regression to return dictionary - 2.2.1: - - changed all references to parent package analysis to tra_analysis - 2.2.0: - - added Sort class - - added several array sorting functions to Sort class including: - - quick sort - - merge sort - - intro(spective) sort - - heap sort - - insertion sort - - tim sort - - selection sort - - bubble sort - - cycle sort - - cocktail sort - - tested all sorting algorithms with both lists and numpy arrays - - depreciated sort function from Array class - - added warnings as an import - 2.1.4: - - added sort and search functions to Array class - 2.1.3: - - changed output of basic_stats and histo_analysis to libraries - - fixed __all__ - 2.1.2: - - renamed ArrayTest class to Array - 2.1.1: - - added add, mul, neg, and inv functions to ArrayTest class - - added normalize function to ArrayTest class - - added dot and cross functions to ArrayTest class - 2.1.0: - - added ArrayTest class - - added elementwise mean, median, standard deviation, variance, min, max functions to ArrayTest class - - added elementwise_stats to ArrayTest which encapsulates elementwise statistics - - appended to __all__ to reflect changes - 2.0.6: - - renamed func functions in regression to lin, log, exp, and sig - 2.0.5: - - moved random_forrest_regressor and random_forrest_classifier to RandomForrest class - - renamed Metrics to Metric - - renamed RegressionMetrics to RegressionMetric - - renamed ClassificationMetrics to ClassificationMetric - - renamed CorrelationTests to CorrelationTest - - renamed StatisticalTests to StatisticalTest - - reflected rafactoring to all mentions of above classes/functions - 2.0.4: - - fixed __all__ to reflected the correct functions and classes - - fixed CorrelationTests and StatisticalTests class functions to require self invocation - - added missing math import - - fixed KNN class functions to require self invocation - - fixed Metrics class functions to require self invocation - - various spelling fixes in CorrelationTests and StatisticalTests - 2.0.3: - - bug fixes with CorrelationTests and StatisticalTests - - moved glicko2 and trueskill to the metrics subpackage - - moved elo to a new metrics subpackage - 2.0.2: - - fixed docs - 2.0.1: - - fixed docs - 2.0.0: - - cleaned up wild card imports with scipy and sklearn - - added CorrelationTests class - - added StatisticalTests class - - added several correlation tests to CorrelationTests - - added several statistical tests to StatisticalTests - 1.13.9: - - moved elo, glicko2, trueskill functions under class Metrics - 1.13.8: - - moved Glicko2 to a seperate package - 1.13.7: - - fixed bug with trueskill - 1.13.6: - - cleaned up imports - 1.13.5: - - cleaned up package - 1.13.4: - - small fixes to regression to improve performance - 1.13.3: - - filtered nans from regression - 1.13.2: - - removed torch requirement, and moved Regression back to regression.py - 1.13.1: - - bug fix with linear regression not returning a proper value - - cleaned up regression - - fixed bug with polynomial regressions - 1.13.0: - - fixed all regressions to now properly work - 1.12.6: - - fixed bg with a division by zero in histo_analysis - 1.12.5: - - fixed numba issues by removing numba from elo, glicko2 and trueskill - 1.12.4: - - renamed gliko to glicko - 1.12.3: - - removed depreciated code - 1.12.2: - - removed team first time trueskill instantiation in favor of integration in superscript.py - 1.12.1: - - improved readibility of regression outputs by stripping tensor data - - used map with lambda to acheive the improved readibility - - lost numba jit support with regression, and generated_jit hangs at execution - - TODO: reimplement correct numba integration in regression - 1.12.0: - - temporarily fixed polynomial regressions by using sklearn's PolynomialFeatures - 1.11.010: - - alphabeticaly ordered import lists - 1.11.9: - - bug fixes - 1.11.8: - - bug fixes - 1.11.7: - - bug fixes - 1.11.6: - - tested min and max - - bug fixes - 1.11.5: - - added min and max in basic_stats - 1.11.4: - - bug fixes - 1.11.3: - - bug fixes - 1.11.2: - - consolidated metrics - - fixed __all__ - 1.11.1: - - added test/train split to RandomForestClassifier and RandomForestRegressor - 1.11.0: - - added RandomForestClassifier and RandomForestRegressor - - note: untested - 1.10.0: - - added numba.jit to remaining functions - 1.9.2: - - kernelized PCA and KNN - 1.9.1: - - fixed bugs with SVM and NaiveBayes - 1.9.0: - - added SVM class, subclasses, and functions - - note: untested - 1.8.0: - - added NaiveBayes classification engine - - note: untested - 1.7.0: - - added knn() - - added confusion matrix to decisiontree() - 1.6.2: - - changed layout of __changelog to be vscode friendly - 1.6.1: - - added additional hyperparameters to decisiontree() - 1.6.0: - - fixed __version__ - - fixed __all__ order - - added decisiontree() - 1.5.3: - - added pca - 1.5.2: - - reduced import list - - added kmeans clustering engine - 1.5.1: - - simplified regression by using .to(device) - 1.5.0: - - added polynomial regression to regression(); untested - 1.4.0: - - added trueskill() - 1.3.2: - - renamed regression class to Regression, regression_engine() to regression gliko2_engine class to Gliko2 - 1.3.1: - - changed glicko2() to return tuple instead of array - 1.3.0: - - added glicko2_engine class and glicko() - - verified glicko2() accuracy - 1.2.3: - - fixed elo() - 1.2.2: - - added elo() - - elo() has bugs to be fixed - 1.2.1: - - readded regrression import - 1.2.0: - - integrated regression.py as regression class - - removed regression import - - fixed metadata for regression class - - fixed metadata for analysis class - 1.1.1: - - regression_engine() bug fixes, now actaully regresses - 1.1.0: - - added regression_engine() - - added all regressions except polynomial - 1.0.7: - - updated _init_device() - 1.0.6: - - removed useless try statements - 1.0.5: - - removed impossible outcomes - 1.0.4: - - added performance metrics (r^2, mse, rms) - 1.0.3: - - resolved nopython mode for mean, median, stdev, variance - 1.0.2: - - snapped (removed) majority of uneeded imports - - forced object mode (bad) on all jit - - TODO: stop numba complaining about not being able to compile in nopython mode - 1.0.1: - - removed from sklearn import * to resolve uneeded wildcard imports - 1.0.0: - - removed c_entities,nc_entities,obstacles,objectives from __all__ - - applied numba.jit to all functions - - depreciated and removed stdev_z_split - - cleaned up histo_analysis to include numpy and numba.jit optimizations - - depreciated and removed all regression functions in favor of future pytorch optimizer - - depreciated and removed all nonessential functions (basic_analysis, benchmark, strip_data) - - optimized z_normalize using sklearn.preprocessing.normalize - - TODO: implement kernel/function based pytorch regression optimizer - 0.9.0: - - refactored - - numpyed everything - - removed stats in favor of numpy functions - 0.8.5: - - minor fixes - 0.8.4: - - removed a few unused dependencies - 0.8.3: - - added p_value function - 0.8.2: - - updated __all__ correctly to contain changes made in v 0.8.0 and v 0.8.1 - 0.8.1: - - refactors - - bugfixes - 0.8.0: - - depreciated histo_analysis_old - - depreciated debug - - altered basic_analysis to take array data instead of filepath - - refactor - - optimization - 0.7.2: - - bug fixes - 0.7.1: - - bug fixes - 0.7.0: - - added tanh_regression (logistical regression) - - bug fixes - 0.6.5: - - added z_normalize function to normalize dataset - - bug fixes - 0.6.4: - - bug fixes - 0.6.3: - - bug fixes - 0.6.2: - - bug fixes - 0.6.1: - - corrected __all__ to contain all of the functions - 0.6.0: - - added calc_overfit, which calculates two measures of overfit, error and performance - - added calculating overfit to optimize_regression - 0.5.0: - - added optimize_regression function, which is a sample function to find the optimal regressions - - optimize_regression function filters out some overfit funtions (functions with r^2 = 1) - - planned addition: overfit detection in the optimize_regression function - 0.4.2: - - added __changelog__ - - updated debug function with log and exponential regressions - 0.4.1: - - added log regressions - - added exponential regressions - - added log_regression and exp_regression to __all__ - 0.3.8: - - added debug function to further consolidate functions - 0.3.7: - - added builtin benchmark function - - added builtin random (linear) data generation function - - added device initialization (_init_device) - 0.3.6: - - reorganized the imports list to be in alphabetical order - - added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives - 0.3.5: - - major bug fixes - - updated historical analysis - - depreciated old historical analysis - 0.3.4: - - added __version__, __author__, __all__ - - added polynomial regression - - added root mean squared function - - added r squared function - 0.3.3: - - bug fixes - - added c_entities - 0.3.2: - - bug fixes - - added nc_entities, obstacles, objectives - - consolidated statistics.py to analysis.py - 0.3.1: - - compiled 1d, column, and row basic stats into basic stats function - 0.3.0: - - added historical analysis function - 0.2.x: - - added z score test - 0.1.x: - - major bug fixes - 0.0.x: - - added loading csv - - added 1d, column, row basic stats -""" - -__author__ = ( - "Arthur Lu ", - "Jacob Levine ", -) - -__all__ = [ - 'load_csv', - 'basic_stats', - 'z_score', - 'z_normalize', - 'histo_analysis', - 'regression', - 'Metric', - 'RegressionMetric', - 'ClassificationMetric', - 'kmeans', - 'pca', - 'decisiontree', - 'KNN', - 'NaiveBayes', - 'SVM', - 'RandomForrest', - 'CorrelationTest', - 'StatisticalTest', - 'Array', - # all statistics functions left out due to integration in other functions -] - -# now back to your regularly scheduled programming: - -# imports (now in alphabetical order! v 0.3.006): - -import csv -from tra_analysis.metrics import elo as Elo -from tra_analysis.metrics import glicko2 as Glicko2 -import math -import numba -from numba import jit -import numpy as np -import scipy -from scipy import optimize, stats -import sklearn -from sklearn import preprocessing, pipeline, linear_model, metrics, cluster, decomposition, tree, neighbors, naive_bayes, svm, model_selection, ensemble -from tra_analysis.metrics import trueskill as Trueskill -import warnings - -class error(ValueError): - pass - -def load_csv(filepath): - with open(filepath, newline='') as csvfile: - file_array = np.array(list(csv.reader(csvfile))) - csvfile.close() - return file_array - -# expects 1d array -@jit(forceobj=True) -def basic_stats(data): - - data_t = np.array(data).astype(float) - - _mean = mean(data_t) - _median = median(data_t) - _stdev = stdev(data_t) - _variance = variance(data_t) - _min = npmin(data_t) - _max = npmax(data_t) - - return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max} - -# returns z score with inputs of point, mean and standard deviation of spread -@jit(forceobj=True) -def z_score(point, mean, stdev): - score = (point - mean) / stdev - - return score - -# expects 2d array, normalizes across all axes -@jit(forceobj=True) -def z_normalize(array, *args): - - array = np.array(array) - for arg in args: - array = sklearn.preprocessing.normalize(array, axis = arg) - - return array - -@jit(forceobj=True) -# expects 2d array of [x,y] -def histo_analysis(hist_data): - - if len(hist_data[0]) > 2: - - hist_data = np.array(hist_data) - derivative = np.array(len(hist_data) - 1, dtype = float) - t = np.diff(hist_data) - derivative = t[1] / t[0] - np.sort(derivative) - - return {"mean": basic_stats(derivative)["mean"], "deviation": basic_stats(derivative)["standard-deviation"]} - - else: - - return None - -def regression(inputs, outputs, args): # inputs, outputs expects N-D array - - X = np.array(inputs) - y = np.array(outputs) - - regressions = {} - - if 'lin' in args: # formula: ax + b - - try: - - def lin(x, a, b): - - return a * x + b - - popt, pcov = scipy.optimize.curve_fit(lin, X, y) - - coeffs = popt.flatten().tolist() - regressions["lin"] = (str(coeffs[0]) + "*x+" + str(coeffs[1])) - - except Exception as e: - - pass - - if 'log' in args: # formula: a log (b(x + c)) + d - - try: - - def log(x, a, b, c, d): - - return a * np.log(b*(x + c)) + d - - popt, pcov = scipy.optimize.curve_fit(log, X, y) - - coeffs = popt.flatten().tolist() - regressions["log"] = (str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3])) - - except Exception as e: - - pass - - if 'exp' in args: # formula: a e ^ (b(x + c)) + d - - try: - - def exp(x, a, b, c, d): - - return a * np.exp(b*(x + c)) + d - - popt, pcov = scipy.optimize.curve_fit(exp, X, y) - - coeffs = popt.flatten().tolist() - regressions["exp"] = (str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3])) - - except Exception as e: - - pass - - if 'ply' in args: # formula: a + bx^1 + cx^2 + dx^3 + ... - - inputs = np.array([inputs]) - outputs = np.array([outputs]) - - plys = {} - limit = len(outputs[0]) - - for i in range(2, limit): - - model = sklearn.preprocessing.PolynomialFeatures(degree = i) - model = sklearn.pipeline.make_pipeline(model, sklearn.linear_model.LinearRegression()) - model = model.fit(np.rot90(inputs), np.rot90(outputs)) - - params = model.steps[1][1].intercept_.tolist() - params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::]) - params = params.flatten().tolist() - - temp = "" - counter = 0 - for param in params: - temp += "(" + str(param) + "*x^" + str(counter) + ")" - counter += 1 - plys["x^" + str(i)] = (temp) - - regressions["ply"] = (plys) - - if 'sig' in args: # formula: a tanh (b(x + c)) + d - - try: - - def sig(x, a, b, c, d): - - return a * np.tanh(b*(x + c)) + d - - popt, pcov = scipy.optimize.curve_fit(sig, X, y) - - coeffs = popt.flatten().tolist() - regressions["sig"] = (str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3])) - - except Exception as e: - - pass - - return regressions - -class Metric: - - def elo(self, starting_score, opposing_score, observed, N, K): - - return Elo.calculate(starting_score, opposing_score, observed, N, K) - - def glicko2(self, starting_score, starting_rd, starting_vol, opposing_score, opposing_rd, observations): - - player = Glicko2.Glicko2(rating = starting_score, rd = starting_rd, vol = starting_vol) - - player.update_player([x for x in opposing_score], [x for x in opposing_rd], observations) - - return (player.rating, player.rd, player.vol) - - def trueskill(self, teams_data, observations): # teams_data is array of array of tuples ie. [[(mu, sigma), (mu, sigma), (mu, sigma)], [(mu, sigma), (mu, sigma), (mu, sigma)]] - - team_ratings = [] - - for team in teams_data: - team_temp = () - for player in team: - player = Trueskill.Rating(player[0], player[1]) - team_temp = team_temp + (player,) - team_ratings.append(team_temp) - - return Trueskill.rate(team_ratings, ranks=observations) - -class RegressionMetric(): - - def __new__(cls, predictions, targets): - - return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets) - - def r_squared(self, predictions, targets): # assumes equal size inputs - - return sklearn.metrics.r2_score(targets, predictions) - - def mse(self, predictions, targets): - - return sklearn.metrics.mean_squared_error(targets, predictions) - - def rms(self, predictions, targets): - - return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions)) - -class ClassificationMetric(): - - def __new__(cls, predictions, targets): - - return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets) - - def cm(self, predictions, targets): - - return sklearn.metrics.confusion_matrix(targets, predictions) - - def cr(self, predictions, targets): - - return sklearn.metrics.classification_report(targets, predictions) - -@jit(nopython=True) -def mean(data): - - return np.mean(data) - -@jit(nopython=True) -def median(data): - - return np.median(data) - -@jit(nopython=True) -def stdev(data): - - return np.std(data) - -@jit(nopython=True) -def variance(data): - - return np.var(data) - -@jit(nopython=True) -def npmin(data): - - return np.amin(data) - -@jit(nopython=True) -def npmax(data): - - return np.amax(data) - -@jit(forceobj=True) -def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"): - - kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm) - kernel.fit(data) - predictions = kernel.predict(data) - centers = kernel.cluster_centers_ - - return centers, predictions - -@jit(forceobj=True) -def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None): - - kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state) - - return kernel.fit_transform(data) - -@jit(forceobj=True) -def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.tree.DecisionTreeClassifier(criterion = criterion, splitter = splitter, max_depth = max_depth) - model = model.fit(data_train,labels_train) - predictions = model.predict(data_test) - metrics = ClassificationMetric(predictions, labels_test) - - return model, metrics - -class KNN: - - def knn_classifier(self, data, labels, n_neighbors, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.neighbors.KNeighborsClassifier() - model.fit(data_train, labels_train) - predictions = model.predict(data_test) - - return model, ClassificationMetric(predictions, labels_test) - - def knn_regressor(self, data, outputs, n_neighbors, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None): - - data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) - model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs) - model.fit(data_train, outputs_train) - predictions = model.predict(data_test) - - return model, RegressionMetric(predictions, outputs_test) - -class NaiveBayes: - - def guassian(self, data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09): - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing) - model.fit(data_train, labels_train) - predictions = model.predict(data_test) - - return model, ClassificationMetric(predictions, labels_test) - - def multinomial(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None): - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.naive_bayes.MultinomialNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior) - model.fit(data_train, labels_train) - predictions = model.predict(data_test) - - return model, ClassificationMetric(predictions, labels_test) - - def bernoulli(self, data, labels, test_size = 0.3, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None): - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.naive_bayes.BernoulliNB(alpha = alpha, binarize = binarize, fit_prior = fit_prior, class_prior = class_prior) - model.fit(data_train, labels_train) - predictions = model.predict(data_test) - - return model, ClassificationMetric(predictions, labels_test) - - def complement(self, data, labels, test_size = 0.3, alpha=1.0, fit_prior=True, class_prior=None, norm=False): - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.naive_bayes.ComplementNB(alpha = alpha, fit_prior = fit_prior, class_prior = class_prior, norm = norm) - model.fit(data_train, labels_train) - predictions = model.predict(data_test) - - return model, ClassificationMetric(predictions, labels_test) - -class SVM: - - class CustomKernel: - - def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state): - - return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) - - class StandardKernel: - - def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None): - - return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) - - class PrebuiltKernel: - - class Linear: - - def __new__(cls): - - return sklearn.svm.SVC(kernel = 'linear') - - class Polynomial: - - def __new__(cls, power, r_bias): - - return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias) - - class RBF: - - def __new__(cls, gamma): - - return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma) - - class Sigmoid: - - def __new__(cls, r_bias): - - return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias) - - def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs - - return kernel.fit(train_data, train_outputs) - - def eval_classification(self, kernel, test_data, test_outputs): - - predictions = kernel.predict(test_data) - - return ClassificationMetric(predictions, test_outputs) - - def eval_regression(self, kernel, test_data, test_outputs): - - predictions = kernel.predict(test_data) - - return RegressionMetric(predictions, test_outputs) - -class RandomForest: - - def random_forest_classifier(self, data, labels, test_size, n_estimators, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None): - - data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight) - kernel.fit(data_train, labels_train) - predictions = kernel.predict(data_test) - - return kernel, ClassificationMetric(predictions, labels_test) - - def random_forest_regressor(self, data, outputs, test_size, n_estimators, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False): - - data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) - kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start) - kernel.fit(data_train, outputs_train) - predictions = kernel.predict(data_test) - - return kernel, RegressionMetric(predictions, outputs_test) - -class CorrelationTest: - - def anova_oneway(self, *args): #expects arrays of samples - - results = scipy.stats.f_oneway(*args) - return {"f-value": results[0], "p-value": results[1]} - - def pearson(self, x, y): - - results = scipy.stats.pearsonr(x, y) - return {"r-value": results[0], "p-value": results[1]} - - def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'): - - results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy) - return {"r-value": results[0], "p-value": results[1]} - - def point_biserial(self, x,y): - - results = scipy.stats.pointbiserialr(x, y) - return {"r-value": results[0], "p-value": results[1]} - - def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'): - - results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method) - return {"tau": results[0], "p-value": results[1]} - - def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True): - - results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive) - return {"tau": results[0], "p-value": results[1]} - - def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None): - - results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state) - return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value - -class StatisticalTest: - - def ttest_onesample(self, a, popmean, axis = 0, nan_policy = 'propagate'): - - results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy) - return {"t-value": results[0], "p-value": results[1]} - - def ttest_independent(self, a, b, equal = True, nan_policy = 'propagate'): - - results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy) - return {"t-value": results[0], "p-value": results[1]} - - def ttest_statistic(self, o1, o2, equal = True): - - results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal) - return {"t-value": results[0], "p-value": results[1]} - - def ttest_related(self, a, b, axis = 0, nan_policy='propagate'): - - results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy) - return {"t-value": results[0], "p-value": results[1]} - - def ks_fitness(self, rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'): - - results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode) - return {"ks-value": results[0], "p-value": results[1]} - - def chisquare(self, f_obs, f_exp = None, ddof = None, axis = 0): - - results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis) - return {"chisquared-value": results[0], "p-value": results[1]} - - def powerdivergence(self, f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None): - - results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_) - return {"powerdivergence-value": results[0], "p-value": results[1]} - - def ks_twosample(self, x, y, alternative = 'two_sided', mode = 'auto'): - - results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode) - return {"ks-value": results[0], "p-value": results[1]} - - def es_twosample(self, x, y, t = (0.4, 0.8)): - - results = scipy.stats.epps_singleton_2samp(x, y, t = t) - return {"es-value": results[0], "p-value": results[1]} - - def mw_rank(self, x, y, use_continuity = True, alternative = None): - - results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative) - return {"u-value": results[0], "p-value": results[1]} - - def mw_tiecorrection(self, rank_values): - - results = scipy.stats.tiecorrect(rank_values) - return {"correction-factor": results} - - def rankdata(self, a, method = 'average'): - - results = scipy.stats.rankdata(a, method = method) - return results - - def wilcoxon_ranksum(self, a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test - - results = scipy.stats.ranksums(a, b) - return {"u-value": results[0], "p-value": results[1]} - - def wilcoxon_signedrank(self, x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'): - - results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative) - return {"t-value": results[0], "p-value": results[1]} - - def kw_htest(self, *args, nan_policy = 'propagate'): - - results = scipy.stats.kruskal(*args, nan_policy = nan_policy) - return {"h-value": results[0], "p-value": results[1]} - - def friedman_chisquare(self, *args): - - results = scipy.stats.friedmanchisquare(*args) - return {"chisquared-value": results[0], "p-value": results[1]} - - def bm_wtest(self, x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'): - - results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy) - return {"w-value": results[0], "p-value": results[1]} - - def combine_pvalues(self, pvalues, method = 'fisher', weights = None): - - results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights) - return {"combined-statistic": results[0], "p-value": results[1]} - - def jb_fitness(self, x): - - results = scipy.stats.jarque_bera(x) - return {"jb-value": results[0], "p-value": results[1]} - - def ab_equality(self, x, y): - - results = scipy.stats.ansari(x, y) - return {"ab-value": results[0], "p-value": results[1]} - - def bartlett_variance(self, *args): - - results = scipy.stats.bartlett(*args) - return {"t-value": results[0], "p-value": results[1]} - - def levene_variance(self, *args, center = 'median', proportiontocut = 0.05): - - results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut) - return {"w-value": results[0], "p-value": results[1]} - - def sw_normality(self, x): - - results = scipy.stats.shapiro(x) - return {"w-value": results[0], "p-value": results[1]} - - def shapiro(self, x): - - return "destroyed by facts and logic" - - def ad_onesample(self, x, dist = 'norm'): - - results = scipy.stats.anderson(x, dist = dist) - return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]} - - def ad_ksample(self, samples, midrank = True): - - results = scipy.stats.anderson_ksamp(samples, midrank = midrank) - return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]} - - def binomial(self, x, n = None, p = 0.5, alternative = 'two-sided'): - - results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative) - return {"p-value": results} - - def fk_variance(self, *args, center = 'median', proportiontocut = 0.05): - - results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut) - return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value - - def mood_mediantest(self, *args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'): - - results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy) - return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]} - - def mood_equalscale(self, x, y, axis = 0): - - results = scipy.stats.mood(x, y, axis = axis) - return {"z-score": results[0], "p-value": results[1]} - - def skewtest(self, a, axis = 0, nan_policy = 'propogate'): - - results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy) - return {"z-score": results[0], "p-value": results[1]} - - def kurtosistest(self, a, axis = 0, nan_policy = 'propogate'): - - results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy) - return {"z-score": results[0], "p-value": results[1]} - - def normaltest(self, a, axis = 0, nan_policy = 'propogate'): - - results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy) - return {"z-score": results[0], "p-value": results[1]} - -class Array(): # tests on nd arrays independent of basic_stats - - def __init__(self, narray): - - self.array = np.array(narray) - - def __str__(self): - - return str(self.array) - - def elementwise_mean(self, *args, axis = 0): # expects arrays that are size normalized - if len(*args) == 0: - return np.mean(self.array, axis = axis) - else: - return np.mean([*args], axis = axis) - - def elementwise_median(self, *args, axis = 0): - - if len(*args) == 0: - return np.median(self.array, axis = axis) - else: - return np.median([*args], axis = axis) - - def elementwise_stdev(self, *args, axis = 0): - - if len(*args) == 0: - return np.std(self.array, axis = axis) - else: - return np.std([*args], axis = axis) - - def elementwise_variance(self, *args, axis = 0): - - if len(*args) == 0: - return np.var(self.array, axis = axis) - else: - return np.var([*args], axis = axis) - - def elementwise_npmin(self, *args, axis = 0): - - if len(*args) == 0: - return np.amin(self.array, axis = axis) - else: - return np.amin([*args], axis = axis) - - def elementwise_npmax(self, *args, axis = 0): - - if len(*args) == 0: - return np.amax(self.array, axis = axis) - else: - return np.amax([*args], axis = axis) - - def elementwise_stats(self, *args, axis = 0): - - _mean = self.elementwise_mean(*args, axis = axis) - _median = self.elementwise_median(*args, axis = axis) - _stdev = self.elementwise_stdev(*args, axis = axis) - _variance = self.elementwise_variance(*args, axis = axis) - _min = self.elementwise_npmin(*args, axis = axis) - _max = self.elementwise_npmax(*args, axis = axis) - - return _mean, _median, _stdev, _variance, _min, _max - - def __getitem__(self, key): - - return self.array[key] - - def __setitem__(self, key, value): - - self.array[key] == value - - def normalize(self, array): - - a = np.atleast_1d(np.linalg.norm(array)) - a[a==0] = 1 - return array / np.expand_dims(a, -1) - - def __add__(self, other): - - return self.array + other.array - - def __sub__(self, other): - - return self.array - other.array - - def __neg__(self): - - return -self.array - - def __abs__(self): - - return abs(self.array) - - def __invert__(self): - - return 1/self.array - - def __mul__(self, other): - - return self.array.dot(other.array) - - def __rmul__(self, other): - - return self.array.dot(other.array) - - def cross(self, other): - - return np.cross(self.array, other.array) - - def sort(self, array): # depreciated - warnings.warn("Array.sort has been depreciated in favor of Sort") - array_length = len(array) - if array_length <= 1: - return array - middle_index = int(array_length / 2) - left = array[0:middle_index] - right = array[middle_index:] - left = self.sort(left) - right = self.sort(right) - return self.__merge(left, right) - - - def __merge(self, left, right): - sorted_list = [] - left = left[:] - right = right[:] - while len(left) > 0 or len(right) > 0: - if len(left) > 0 and len(right) > 0: - if left[0] <= right[0]: - sorted_list.append(left.pop(0)) - else: - sorted_list.append(right.pop(0)) - elif len(left) > 0: - sorted_list.append(left.pop(0)) - elif len(right) > 0: - sorted_list.append(right.pop(0)) - return sorted_list - - def search(self, arr, x): - return self.__search(arr, 0, len(arr) - 1, x) - - def __search(self, arr, low, high, x): - if high >= low: - mid = (high + low) // 2 - if arr[mid] == x: - return mid - elif arr[mid] > x: - return binary_search(arr, low, mid - 1, x) - else: - return binary_search(arr, mid + 1, high, x) - else: - return -1 - -class Sort: # if you haven't used a sort, then you've never lived - - def quicksort(self, a): - - def sort(array): - - less = [] - equal = [] - greater = [] - - if len(array) > 1: - pivot = array[0] - for x in array: - if x < pivot: - less.append(x) - elif x == pivot: - equal.append(x) - elif x > pivot: - greater.append(x) - return sort(less)+equal+sort(greater) - else: - return array - - return np.array(sort(a)) - - def mergesort(self, a): - - def sort(array): - - array = array - - if len(array) >1: - middle = len(array) // 2 - L = array[:middle] - R = array[middle:] - - sort(L) - sort(R) - - i = j = k = 0 - - while i < len(L) and j < len(R): - if L[i] < R[j]: - array[k] = L[i] - i+= 1 - else: - array[k] = R[j] - j+= 1 - k+= 1 - - while i < len(L): - array[k] = L[i] - i+= 1 - k+= 1 - - while j < len(R): - array[k] = R[j] - j+= 1 - k+= 1 - - return array - - return sort(a) - - def introsort(self, a): - - def sort(array, start, end, maxdepth): - - array = array - - if end - start <= 1: - return - elif maxdepth == 0: - heapsort(array, start, end) - else: - p = partition(array, start, end) - sort(array, start, p + 1, maxdepth - 1) - sort(array, p + 1, end, maxdepth - 1) - - return array - - def partition(array, start, end): - pivot = array[start] - i = start - 1 - j = end - - while True: - i = i + 1 - while array[i] < pivot: - i = i + 1 - j = j - 1 - while array[j] > pivot: - j = j - 1 - - if i >= j: - return j - - swap(array, i, j) - - def swap(array, i, j): - array[i], array[j] = array[j], array[i] - - def heapsort(array, start, end): - build_max_heap(array, start, end) - for i in range(end - 1, start, -1): - swap(array, start, i) - max_heapify(array, index=0, start=start, end=i) - - def build_max_heap(array, start, end): - def parent(i): - return (i - 1)//2 - length = end - start - index = parent(length - 1) - while index >= 0: - max_heapify(array, index, start, end) - index = index - 1 - - def max_heapify(array, index, start, end): - def left(i): - return 2*i + 1 - def right(i): - return 2*i + 2 - - size = end - start - l = left(index) - r = right(index) - if (l < size and array[start + l] > array[start + index]): - largest = l - else: - largest = index - if (r < size and array[start + r] > array[start + largest]): - largest = r - if largest != index: - swap(array, start + largest, start + index) - max_heapify(array, largest, start, end) - - maxdepth = (len(a).bit_length() - 1)*2 - - return sort(a, 0, len(a), maxdepth) - - def heapsort(self, a): - - def sort(array): - - array = array - - n = len(array) - - for i in range(n//2 - 1, -1, -1): - heapify(array, n, i) - - for i in range(n-1, 0, -1): - array[i], array[0] = array[0], array[i] - heapify(array, i, 0) - - return array - - def heapify(array, n, i): - - array = array - - largest = i - l = 2 * i + 1 - r = 2 * i + 2 - - if l < n and array[i] < array[l]: - largest = l - - if r < n and array[largest] < array[r]: - largest = r - - if largest != i: - array[i],array[largest] = array[largest],array[i] - heapify(array, n, largest) - - return array - - return sort(a) - - def insertionsort(self, a): - - def sort(array): - - array = array - - for i in range(1, len(array)): - - key = array[i] - - j = i-1 - while j >=0 and key < array[j] : - array[j+1] = array[j] - j -= 1 - array[j+1] = key - - return array - - return sort(a) - - def timsort(self, a, block = 32): - - BLOCK = block - - def sort(array, n): - - array = array - - for i in range(0, n, BLOCK): - insertionsort(array, i, min((i+31), (n-1))) - - size = BLOCK - while size < n: - - for left in range(0, n, 2*size): - - mid = left + size - 1 - right = min((left + 2*size - 1), (n-1)) - merge(array, left, mid, right) - - size = 2*size - - return array - - def insertionsort(array, left, right): - - array = array - - for i in range(left + 1, right+1): - - temp = array[i] - j = i - 1 - while j >= left and array[j] > temp : - - array[j+1] = array[j] - j -= 1 - - array[j+1] = temp - - return array - - - def merge(array, l, m, r): - - len1, len2 = m - l + 1, r - m - left, right = [], [] - for i in range(0, len1): - left.append(array[l + i]) - for i in range(0, len2): - right.append(array[m + 1 + i]) - - i, j, k = 0, 0, l - - while i < len1 and j < len2: - - if left[i] <= right[j]: - array[k] = left[i] - i += 1 - - else: - array[k] = right[j] - j += 1 - - k += 1 - - while i < len1: - - array[k] = left[i] - k += 1 - i += 1 - - while j < len2: - array[k] = right[j] - k += 1 - j += 1 - - return sort(a, len(a)) - - def selectionsort(self, a): - array = a - for i in range(len(array)): - min_idx = i - for j in range(i+1, len(array)): - if array[min_idx] > array[j]: - min_idx = j - array[i], array[min_idx] = array[min_idx], array[i] - return array - - def shellsort(self, a): - array = a - n = len(array) - gap = n//2 - - while gap > 0: - - for i in range(gap,n): - - temp = array[i] - j = i - while j >= gap and array[j-gap] >temp: - array[j] = array[j-gap] - j -= gap - array[j] = temp - gap //= 2 - - return array - - def bubblesort(self, a): - - def sort(array): - for i, num in enumerate(array): - try: - if array[i+1] < num: - array[i] = array[i+1] - array[i+1] = num - sort(array) - except IndexError: - pass - return array - - return sort(a) - - def cyclesort(self, a): - - def sort(array): - - array = array - writes = 0 - - for cycleStart in range(0, len(array) - 1): - item = array[cycleStart] - - pos = cycleStart - for i in range(cycleStart + 1, len(array)): - if array[i] < item: - pos += 1 - - if pos == cycleStart: - continue - - while item == array[pos]: - pos += 1 - array[pos], item = item, array[pos] - writes += 1 - - while pos != cycleStart: - - pos = cycleStart - for i in range(cycleStart + 1, len(array)): - if array[i] < item: - pos += 1 - - while item == array[pos]: - pos += 1 - array[pos], item = item, array[pos] - writes += 1 - - return array - - return sort(a) - - def cocktailsort(self, a): - - def sort(array): - - array = array - - n = len(array) - swapped = True - start = 0 - end = n-1 - while (swapped == True): - swapped = False - for i in range (start, end): - if (array[i] > array[i + 1]) : - array[i], array[i + 1]= array[i + 1], array[i] - swapped = True - if (swapped == False): - break - swapped = False - end = end-1 - for i in range(end-1, start-1, -1): - if (array[i] > array[i + 1]): - array[i], array[i + 1] = array[i + 1], array[i] - swapped = True - start = start + 1 - - return array - - return sort(a) \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation.ipynb b/analysis-master/tra_analysis/equation.ipynb deleted file mode 100644 index 0d8ef4ea..00000000 --- a/analysis-master/tra_analysis/equation.ipynb +++ /dev/null @@ -1,162 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "from decimal import Decimal\n", - "from functools import reduce" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def add(string):\n", - " while(len(re.findall(\"[+]{1}[-]?\", string)) != 0):\n", - " string = re.sub(\"[-]?\\d+[.]?\\d*[+]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x + y), [Decimal(i) for i in re.split(\"[+]{1}\", re.search(\"[-]?\\d+[.]?\\d*[+]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n", - " return string" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def sub(string):\n", - " while(len(re.findall(\"\\d+[.]?\\d*[-]{1,2}\\d+[.]?\\d*\", string)) != 0):\n", - " g = re.search(\"\\d+[.]?\\d*[-]{1,2}\\d+[.]?\\d*\", string).group()\n", - " if(re.search(\"[-]{1,2}\", g).group() == \"-\"):\n", - " r = re.sub(\"[-]{1}\", \"+-\", g, 1)\n", - " string = re.sub(g, r, string, 1)\n", - " elif(re.search(\"[-]{1,2}\", g).group() == \"--\"):\n", - " r = re.sub(\"[-]{2}\", \"+\", g, 1)\n", - " string = re.sub(g, r, string, 1)\n", - " else:\n", - " pass\n", - " return string" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def mul(string):\n", - " while(len(re.findall(\"[*]{1}[-]?\", string)) != 0):\n", - " string = re.sub(\"[-]?\\d+[.]?\\d*[*]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x * y), [Decimal(i) for i in re.split(\"[*]{1}\", re.search(\"[-]?\\d+[.]?\\d*[*]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n", - " return string" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def div(string):\n", - " while(len(re.findall(\"[/]{1}[-]?\", string)) != 0):\n", - " string = re.sub(\"[-]?\\d+[.]?\\d*[/]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x / y), [Decimal(i) for i in re.split(\"[/]{1}\", re.search(\"[-]?\\d+[.]?\\d*[/]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n", - " return string" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def exp(string):\n", - " while(len(re.findall(\"[\\^]{1}[-]?\", string)) != 0):\n", - " string = re.sub(\"[-]?\\d+[.]?\\d*[\\^]{1}[-]?\\d+[.]?\\d*\", str(\"%f\" % reduce((lambda x, y: x ** y), [Decimal(i) for i in re.split(\"[\\^]{1}\", re.search(\"[-]?\\d+[.]?\\d*[\\^]{1}[-]?\\d+[.]?\\d*\", string).group())])), string, 1)\n", - " return string" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "def evaluate(string):\n", - " string = exp(string)\n", - " string = div(string)\n", - " string = mul(string)\n", - " string = sub(string)\n", - " print(string)\n", - " string = add(string)\n", - " return string" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "output_type": "error", - "ename": "SyntaxError", - "evalue": "unexpected EOF while parsing (, line 1)", - "traceback": [ - "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m def parentheses(string):\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unexpected EOF while parsing\n" - ] - } - ], - "source": [ - "def parentheses(string):" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": "-158456325028528675187087900672.000000+0.8\n" - }, - { - "output_type": "execute_result", - "data": { - "text/plain": "'-158456325028528675187087900672.000000'" - }, - "metadata": {}, - "execution_count": 22 - } - ], - "source": [ - "string = \"8^32*4/-2+0.8\"\n", - "evaluate(string)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6-final" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/analysis-master/tra_analysis/regression.py b/analysis-master/tra_analysis/regression_old.py similarity index 100% rename from analysis-master/tra_analysis/regression.py rename to analysis-master/tra_analysis/regression_old.py From f04f460d4712ed5d408b89a7e51be72ea43cc297 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Mon, 5 Oct 2020 03:49:27 +0000 Subject: [PATCH 02/24] changed setup.py to use __version__ from source added Topic and keywords Signed-off-by: Arthur Lu --- analysis-master/setup.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/analysis-master/setup.py b/analysis-master/setup.py index 9c9cbf28..26a22f93 100644 --- a/analysis-master/setup.py +++ b/analysis-master/setup.py @@ -1,4 +1,5 @@ import setuptools +import tra_analysis requirements = [] @@ -8,11 +9,11 @@ with open("requirements.txt", 'r') as file: setuptools.setup( name="tra_analysis", - version="2.1.0", + version=tra_analysis.__version__, author="The Titan Scouting Team", author_email="titanscout2022@gmail.com", description="Analysis package developed by Titan Scouting for The Red Alliance", - long_description="", + long_description="../README.md", long_description_content_type="text/markdown", url="https://github.com/titanscout2022/tr2022-strategy", packages=setuptools.find_packages(), @@ -21,6 +22,8 @@ setuptools.setup( classifiers=[ "Programming Language :: Python :: 3", "Operating System :: OS Independent", + "Topic :: Data Analysis" ], python_requires='>=3.6', + keywords="data analysis tools" ) \ No newline at end of file From 764dab01f6461388f7076816e55d0934f1ca21de Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Mon, 5 Oct 2020 07:49:39 -0700 Subject: [PATCH 03/24] reflected doc changes to README.md (#48) Signed-off-by: Arthur Lu --- README.md | 77 ++++++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 71 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 598567d7..6d0be0eb 100644 --- a/README.md +++ b/README.md @@ -1,37 +1,102 @@ # Red Alliance Analysis · ![GitHub release (latest by date)](https://img.shields.io/github/v/release/titanscout2022/red-alliance-analysis) + Titan Robotics 2022 Strategy Team Repository for Data Analysis Tools. Included with these tools are the backend data analysis engine formatted as a python package, associated binaries for the analysis package, and premade scripts that can be pulled directly from this repository and will integrate with other Red Alliance applications to quickly deploy FRC scouting tools. -# Getting Started + +--- + +# `tra-analysis` + +`tra-analysis` is a higher level package for data processing and analysis. It is a python library that combines popular data science tools like numpy, scipy, and sklearn along with other tools to create an easy-to-use data analysis engine. tra-analysis includes analysis in all ranges of complexity from basic statistics like mean, median, mode to complex kernel based classifiers and allows user to more quickly deploy these algorithms. The package also includes performance metrics for score based applications including elo, glicko2, and trueskill ranking systems. + +At the core of the tra-analysis package is the modularity of each analytical tool. The package encapsulates the setup code for the included data science tools. For example, there are many packages that allow users to generate many different types of regressions. With the tra-analysis package, one function can be called to generate many regressions and sort them by accuracy. + ## Prerequisites +--- + * Python >= 3.6 * Pip which can be installed by running\ `curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py`\ `python get-pip.py`\ after installing python, or with a package manager on linux. Refer to the [pip installation instructions](https://pip.pypa.io/en/stable/installing/) for more information. + ## Installing -### Standard Platforms +--- + +#### Standard Platforms + For the latest version of tra-analysis, run `pip install tra-analysis` or `pip install tra_analysis`. The requirements for tra-analysis should be automatically installed. -### Exotic Platforms (Android) + +#### Exotic Platforms (Android) + [Termux](https://termux.com/) is recommended for a linux environemnt on Android. Consult the [documentation](https://titanscouting.github.io/analysis/general/installation#exotic-platforms-android) for advice on installing the prerequisites. After installing the prerequisites, the package should be installed normally with `pip install tra-analysis` or `pip install tra_analysis`. + ## Use + +--- + tra-analysis operates like any other python package. Consult the [documentation](https://titanscouting.github.io/analysis/tra_analysis/) for more information. -# Supported Platforms + +## Supported Platforms + +--- + Although any modern 64 bit platform should be supported, the following platforms have been tested to be working: * AMD64 (Tested on Zen, Zen+, and Zen 2) * Intel 64/x86_64/x64 (Tested on Kaby Lake) * ARM64 (Tested on Broadcom BCM2836 SoC, Broadcom BCM2711 SoC) -### + The following OSes have been tested to be working: * Linux Kernel 3.16, 4.4, 4.15, 4.19, 5.4 * Ubuntu 16.04, 18.04, 20.04 * Debian (and Debian derivaives) Jessie, Buster * Windows 7, 10 -### + The following python versions are supported: * python 3.6 (not tested) * python 3.7 * python 3.8 + +--- + +# `data-analysis` + +To facilitate data analysis of collected scouting data in a user firendly tool, we created the data-analysis application. At its core it uses the tra-analysis package to conduct any number of user selected tests on data collected from the TRA scouting app. It uploads these tests back to MongoDB where it can be viewed from the app at any time. + +The data-analysis application also uses the TRA API to interface with MongoDB and uses the TBA API to collect additional data (match win/loss). + +The application can be configured with a configuration tool or by editing the config.json directly. + +## Prerequisites + +--- + +Before installing and using data-analysis, make sure that you have installed the folowing prerequisites: +- A common operating system like **Windows** or (*most*) distributions of **Linux**. BSD may work but has not been tested nor is it reccomended. +- [Python](https://www.python.org/) version **3.6** or higher +- [Pip](https://pip.pypa.io/en/stable/) (installation instructions [here](https://pip.pypa.io/en/stable/installing/)) + +## Installing Requirements + +--- + +Once navigated to the data-analysis folder run `pip install -r requirements.txt` to install all of the required python libraries. + +## Scripts + +--- + +The data-analysis application is a collection of various scripts and one config file. For users, only the main application `superscript.py` and the config file `config.json` are important. + +To run the data-analysis application, navigate to the data-analysis folder once all requirements have been installed and run `python superscript.py`. If you encounter the error: + +`pymongo.errors.ConfigurationError: Empty host (or extra comma in host list).` + +don't worry, you may have just not configured the application correctly, but would otherwise work. Refer to [the documentation](https://titanscouting.github.io/analysis/data_analysis/Config) to learn how to configure data-analysis. + # Contributing + Read our included contributing guidelines (`CONTRIBUTING.md`) for more information and feel free to reach out to any current maintainer for more information. + # Build Statuses ![Analysis Unit Tests](https://github.com/titanscout2022/red-alliance-analysis/workflows/Analysis%20Unit%20Tests/badge.svg) ![Superscript Unit Tests](https://github.com/titanscout2022/red-alliance-analysis/workflows/Superscript%20Unit%20Tests/badge.svg?branch=master) From 78ac2f042b96e80ae21e3bfb900dc05565dfd579 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Fri, 9 Oct 2020 01:30:52 +0000 Subject: [PATCH 04/24] moved required files back to parent Signed-off-by: Arthur Lu --- .github/LICENSE => LICENSE | 0 .github/MAINTAINERS => MAINTAINERS | 0 .github/README.md => README.md | 0 3 files changed, 0 insertions(+), 0 deletions(-) rename .github/LICENSE => LICENSE (100%) rename .github/MAINTAINERS => MAINTAINERS (100%) rename .github/README.md => README.md (100%) diff --git a/.github/LICENSE b/LICENSE similarity index 100% rename from .github/LICENSE rename to LICENSE diff --git a/.github/MAINTAINERS b/MAINTAINERS similarity index 100% rename from .github/MAINTAINERS rename to MAINTAINERS diff --git a/.github/README.md b/README.md similarity index 100% rename from .github/README.md rename to README.md From 717b812b226fa2e7dcc34f221e7ca285b940e08a Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Fri, 9 Oct 2020 01:43:27 +0000 Subject: [PATCH 05/24] moved security back to parent Signed-off-by: Arthur Lu --- .github/SECURITY.md => SECURITY.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename .github/SECURITY.md => SECURITY.md (100%) diff --git a/.github/SECURITY.md b/SECURITY.md similarity index 100% rename from .github/SECURITY.md rename to SECURITY.md From 88030d5331a231e6d3714cfb8445534f9094aa1f Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Fri, 9 Oct 2020 01:44:34 +0000 Subject: [PATCH 06/24] moved security back to parent moved contributing back to parent Signed-off-by: Arthur Lu --- .github/CONTRIBUTING.md => CONTRIBUTING.md | 0 .github/SECURITY.md => SECURITY.md | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename .github/CONTRIBUTING.md => CONTRIBUTING.md (100%) rename .github/SECURITY.md => SECURITY.md (100%) diff --git a/.github/CONTRIBUTING.md b/CONTRIBUTING.md similarity index 100% rename from .github/CONTRIBUTING.md rename to CONTRIBUTING.md diff --git a/.github/SECURITY.md b/SECURITY.md similarity index 100% rename from .github/SECURITY.md rename to SECURITY.md From 42eeadf333f56cee6893b2571e5a11200e4cef02 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Fri, 9 Oct 2020 06:50:12 +0000 Subject: [PATCH 07/24] add PR template Signed-off-by: Arthur Lu --- .github/PULL_REQUEST_TEMPLATE.md | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 .github/PULL_REQUEST_TEMPLATE.md diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 00000000..4030f6fe --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,7 @@ +Fixes # + +## Proposed Changes + + - + - + - From 76d9b80da274257a0c2e19d195ea4930701a50ce Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Fri, 9 Oct 2020 06:51:08 +0000 Subject: [PATCH 08/24] moved to parent folder Signed-off-by: Arthur Lu --- .github/PULL_REQUEST_TEMPLATE.md => PULL_REQUEST_TEMPLATE.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename .github/PULL_REQUEST_TEMPLATE.md => PULL_REQUEST_TEMPLATE.md (100%) diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/PULL_REQUEST_TEMPLATE.md similarity index 100% rename from .github/PULL_REQUEST_TEMPLATE.md rename to PULL_REQUEST_TEMPLATE.md From 7fd564cec4341ce48966261d041b13a9b30774bd Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Fri, 9 Oct 2020 06:54:53 +0000 Subject: [PATCH 09/24] moved meta files to .github folder Signed-off-by: Arthur Lu --- CONTRIBUTING.md => .github/CONTRIBUTING.md | 0 LICENSE => .github/LICENSE | 0 MAINTAINERS => .github/MAINTAINERS | 0 PULL_REQUEST_TEMPLATE.md => .github/PULL_REQUEST_TEMPLATE.md | 0 README.md => .github/README.md | 0 SECURITY.md => .github/SECURITY.md | 0 6 files changed, 0 insertions(+), 0 deletions(-) rename CONTRIBUTING.md => .github/CONTRIBUTING.md (100%) rename LICENSE => .github/LICENSE (100%) rename MAINTAINERS => .github/MAINTAINERS (100%) rename PULL_REQUEST_TEMPLATE.md => .github/PULL_REQUEST_TEMPLATE.md (100%) rename README.md => .github/README.md (100%) rename SECURITY.md => .github/SECURITY.md (100%) diff --git a/CONTRIBUTING.md b/.github/CONTRIBUTING.md similarity index 100% rename from CONTRIBUTING.md rename to .github/CONTRIBUTING.md diff --git a/LICENSE b/.github/LICENSE similarity index 100% rename from LICENSE rename to .github/LICENSE diff --git a/MAINTAINERS b/.github/MAINTAINERS similarity index 100% rename from MAINTAINERS rename to .github/MAINTAINERS diff --git a/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md similarity index 100% rename from PULL_REQUEST_TEMPLATE.md rename to .github/PULL_REQUEST_TEMPLATE.md diff --git a/README.md b/.github/README.md similarity index 100% rename from README.md rename to .github/README.md diff --git a/SECURITY.md b/.github/SECURITY.md similarity index 100% rename from SECURITY.md rename to .github/SECURITY.md From de0791a7fd5646795138c18f4c0bbef43d100493 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Wed, 14 Oct 2020 18:47:44 +0000 Subject: [PATCH 10/24] Analysis.py v 3.0.1 Signed-off-by: Arthur Lu --- analysis-master/requirements.txt | 1 - analysis-master/tra_analysis/Analysis.py | 19 +++---------------- 2 files changed, 3 insertions(+), 17 deletions(-) diff --git a/analysis-master/requirements.txt b/analysis-master/requirements.txt index 1ecfd056..33435d14 100644 --- a/analysis-master/requirements.txt +++ b/analysis-master/requirements.txt @@ -1,4 +1,3 @@ -numba numpy scipy scikit-learn diff --git a/analysis-master/tra_analysis/Analysis.py b/analysis-master/tra_analysis/Analysis.py index d60a79ad..cfe66455 100644 --- a/analysis-master/tra_analysis/Analysis.py +++ b/analysis-master/tra_analysis/Analysis.py @@ -7,10 +7,12 @@ # current benchmark of optimization: 1.33 times faster # setup: -__version__ = "3.0.0" +__version__ = "3.0.1" # changelog should be viewed using print(analysis.__changelog__) __changelog__ = """changelog: + 3.0.1: + - removed numba dependency and calls 3.0.0: - exported several submodules to their own files while preserving backwards compatibility: - Array @@ -376,8 +378,6 @@ import csv from tra_analysis.metrics import elo as Elo from tra_analysis.metrics import glicko2 as Glicko2 import math -import numba -from numba import jit import numpy as np import scipy from scipy import optimize, stats @@ -409,7 +409,6 @@ def load_csv(filepath): return file_array # expects 1d array -@jit(forceobj=True) def basic_stats(data): data_t = np.array(data).astype(float) @@ -424,14 +423,12 @@ def basic_stats(data): return {"mean": _mean, "median": _median, "standard-deviation": _stdev, "variance": _variance, "minimum": _min, "maximum": _max} # returns z score with inputs of point, mean and standard deviation of spread -@jit(forceobj=True) def z_score(point, mean, stdev): score = (point - mean) / stdev return score # expects 2d array, normalizes across all axes -@jit(forceobj=True) def z_normalize(array, *args): array = np.array(array) @@ -440,7 +437,6 @@ def z_normalize(array, *args): return array -@jit(forceobj=True) # expects 2d array of [x,y] def histo_analysis(hist_data): @@ -589,37 +585,30 @@ class Metric: return Trueskill.rate(team_ratings, ranks=observations) -@jit(nopython=True) def mean(data): return np.mean(data) -@jit(nopython=True) def median(data): return np.median(data) -@jit(nopython=True) def stdev(data): return np.std(data) -@jit(nopython=True) def variance(data): return np.var(data) -@jit(nopython=True) def npmin(data): return np.amin(data) -@jit(nopython=True) def npmax(data): return np.amax(data) -@jit(forceobj=True) def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"): kernel = sklearn.cluster.KMeans(n_clusters = n_clusters, init = init, n_init = n_init, max_iter = max_iter, tol = tol, precompute_distances = precompute_distances, verbose = verbose, random_state = random_state, copy_x = copy_x, n_jobs = n_jobs, algorithm = algorithm) @@ -629,14 +618,12 @@ def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0. return centers, predictions -@jit(forceobj=True) def pca(data, n_components = None, copy = True, whiten = False, svd_solver = "auto", tol = 0.0, iterated_power = "auto", random_state = None): kernel = sklearn.decomposition.PCA(n_components = n_components, copy = copy, whiten = whiten, svd_solver = svd_solver, tol = tol, iterated_power = iterated_power, random_state = random_state) return kernel.fit_transform(data) -@jit(forceobj=True) def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "default", max_depth = None): #expects *2d data and 1d labels data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) From ccd4c6bc18ffa441c7a8fc9f06d02c7fa72b8d1e Mon Sep 17 00:00:00 2001 From: James Pan Date: Wed, 14 Oct 2020 22:01:47 -0500 Subject: [PATCH 11/24] updated test_analysis for submodules, and added missing numpy import in Sort.py --- analysis-master/test_analysis.py | 39 +++++++++++++++++----------- analysis-master/tra_analysis/Sort.py | 2 ++ 2 files changed, 26 insertions(+), 15 deletions(-) diff --git a/analysis-master/test_analysis.py b/analysis-master/test_analysis.py index d281b5ef..0cb41003 100644 --- a/analysis-master/test_analysis.py +++ b/analysis-master/test_analysis.py @@ -1,6 +1,15 @@ -from tra_analysis import analysis as an -from tra_analysis import metrics -from tra_analysis import fits +from tra_analysis import Analysis as an +from tra_analysis import Array +from tra_analysis import ClassificationMetric +from tra_analysis import CorrelationTest +from tra_analysis import Fit +from tra_analysis import KNN +from tra_analysis import NaiveBayes +from tra_analysis import RandomForest +from tra_analysis import RegressionMetric +from tra_analysis import Sort +from tra_analysis import StatisticalTest +from tra_analysis import SVM def test_(): test_data_linear = [1, 3, 6, 7, 9] @@ -21,15 +30,15 @@ def test_(): assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0 assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585) #assert an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0]) == [(metrics.trueskill.Rating(mu=21.346, sigma=7.875), metrics.trueskill.Rating(mu=20.415, sigma=7.808), metrics.trueskill.Rating(mu=29.037, sigma=7.170)), (metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=23.225, sigma=6.287))] - assert all(a == b for a, b in zip(an.Sort().quicksort(test_data_scrambled), test_data_sorted)) - assert all(a == b for a, b in zip(an.Sort().mergesort(test_data_scrambled), test_data_sorted)) - assert all(a == b for a, b in zip(an.Sort().introsort(test_data_scrambled), test_data_sorted)) - assert all(a == b for a, b in zip(an.Sort().heapsort(test_data_scrambled), test_data_sorted)) - assert all(a == b for a, b in zip(an.Sort().insertionsort(test_data_scrambled), test_data_sorted)) - assert all(a == b for a, b in zip(an.Sort().timsort(test_data_scrambled), test_data_sorted)) - assert all(a == b for a, b in zip(an.Sort().selectionsort(test_data_scrambled), test_data_sorted)) - assert all(a == b for a, b in zip(an.Sort().shellsort(test_data_scrambled), test_data_sorted)) - assert all(a == b for a, b in zip(an.Sort().bubblesort(test_data_scrambled), test_data_sorted)) - assert all(a == b for a, b in zip(an.Sort().cyclesort(test_data_scrambled), test_data_sorted)) - assert all(a == b for a, b in zip(an.Sort().cocktailsort(test_data_scrambled), test_data_sorted)) - assert fits.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0) \ No newline at end of file + assert all(a == b for a, b in zip(Sort.quicksort(test_data_scrambled), test_data_sorted)) + assert all(a == b for a, b in zip(Sort.mergesort(test_data_scrambled), test_data_sorted)) + assert all(a == b for a, b in zip(Sort.heapsort(test_data_scrambled), test_data_sorted)) + assert all(a == b for a, b in zip(Sort.introsort(test_data_scrambled), test_data_sorted)) + assert all(a == b for a, b in zip(Sort.insertionsort(test_data_scrambled), test_data_sorted)) + assert all(a == b for a, b in zip(Sort.timsort(test_data_scrambled), test_data_sorted)) + assert all(a == b for a, b in zip(Sort.selectionsort(test_data_scrambled), test_data_sorted)) + assert all(a == b for a, b in zip(Sort.shellsort(test_data_scrambled), test_data_sorted)) + assert all(a == b for a, b in zip(Sort.bubblesort(test_data_scrambled), test_data_sorted)) + assert all(a == b for a, b in zip(Sort.cyclesort(test_data_scrambled), test_data_sorted)) + assert all(a == b for a, b in zip(Sort.cocktailsort(test_data_scrambled), test_data_sorted)) + assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0) \ No newline at end of file diff --git a/analysis-master/tra_analysis/Sort.py b/analysis-master/tra_analysis/Sort.py index b6a446ee..68244cea 100644 --- a/analysis-master/tra_analysis/Sort.py +++ b/analysis-master/tra_analysis/Sort.py @@ -19,6 +19,8 @@ __author__ = ( __all__ = [ ] +import numpy as np + def quicksort(a): def sort(array): From e9731951fc972114ceaad53f32a08b8323b1523d Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Thu, 15 Oct 2020 06:20:44 +0000 Subject: [PATCH 12/24] fixed item one of Issue #58 Signed-off-by: Arthur Lu --- .devcontainer/Dockerfile | 4 ++-- .devcontainer/devcontainer.json | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 364066ee..4cb82cc3 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,2 +1,2 @@ -FROM python -WORKDIR ~/ +FROM python:3.8 +WORKDIR ~/ \ No newline at end of file diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index a1f40089..7f737124 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -24,5 +24,5 @@ "ms-python.python", "waderyan.gitblame" ], - "postCreateCommand": "apt install vim -y ; pip install -r data-analysis/requirements.txt ; pip install -r analysis-master/requirements.txt ; pip install pylint ; pip install tra-analysis" + "postCreateCommand": "apt install vim -y ; pip install --no-cache-dir -r data-analysis/requirements.txt ; pip install --no-cache-dir -r analysis-master/requirements.txt ; pip install --no-cache-dir pylint ; pip install --no-cache-dir tra-analysis" } \ No newline at end of file From 62d23b12ae171fdf889990e4d78c350f6ca4a623 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Thu, 15 Oct 2020 06:41:28 +0000 Subject: [PATCH 13/24] readded cache searching in postCreateCommand Signed-off-by: Arthur Lu --- .devcontainer/devcontainer.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 7f737124..3020c639 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -24,5 +24,5 @@ "ms-python.python", "waderyan.gitblame" ], - "postCreateCommand": "apt install vim -y ; pip install --no-cache-dir -r data-analysis/requirements.txt ; pip install --no-cache-dir -r analysis-master/requirements.txt ; pip install --no-cache-dir pylint ; pip install --no-cache-dir tra-analysis" + "postCreateCommand": "apt install vim -y ; pip install -r data-analysis/requirements.txt ; pip install -r analysis-master/requirements.txt ; pip install --no-cache-dir pylint ; pip install --no-cache-dir tra-analysis" } \ No newline at end of file From 23ed22fdff15dd2a3d35fb56154e8deac1e45daf Mon Sep 17 00:00:00 2001 From: James Pan Date: Thu, 15 Oct 2020 14:33:13 -0500 Subject: [PATCH 14/24] added myself as an author --- analysis-master/tra_analysis/Sort.py | 3 ++- analysis-master/tra_analysis/__init__.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/analysis-master/tra_analysis/Sort.py b/analysis-master/tra_analysis/Sort.py index 68244cea..2ff25769 100644 --- a/analysis-master/tra_analysis/Sort.py +++ b/analysis-master/tra_analysis/Sort.py @@ -1,5 +1,5 @@ # Titan Robotics Team 2022: Sort submodule -# Written by Arthur Lu +# Written by Arthur Lu and James Pan # Notes: # this should be imported as a python module using 'from tra_analysis import Sort' # setup: @@ -14,6 +14,7 @@ __changelog__ = """changelog: __author__ = ( "Arthur Lu ", + "James Pan " ) __all__ = [ diff --git a/analysis-master/tra_analysis/__init__.py b/analysis-master/tra_analysis/__init__.py index 6ea9c9b7..5342c0fd 100644 --- a/analysis-master/tra_analysis/__init__.py +++ b/analysis-master/tra_analysis/__init__.py @@ -1,5 +1,5 @@ # Titan Robotics Team 2022: tra_analysis package -# Written by Arthur Lu, Jacob Levine, and Dev Singh +# Written by Arthur Lu, Jacob Levine, Dev Singh, and James Pan # Notes: # this should be imported as a python package using 'import tra_analysis' # this should be included in the local directory or environment variable @@ -20,6 +20,7 @@ __author__ = ( "Arthur Lu ", "Jacob Levine ", "Dev Singh ", + "James Pan " ) __all__ = [ From 5b1c80bfad84acd01b46b84ffde89a47aeb88c43 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Thu, 15 Oct 2020 23:55:41 +0000 Subject: [PATCH 15/24] added Kivy to requirements.txt Signed-off-by: Arthur Lu --- data-analysis/requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/data-analysis/requirements.txt b/data-analysis/requirements.txt index 58d1bd37..f95ca145 100644 --- a/data-analysis/requirements.txt +++ b/data-analysis/requirements.txt @@ -1,4 +1,5 @@ requests pymongo pandas -tra-analysis \ No newline at end of file +tra-analysis +kivy==2.0.0rc2 \ No newline at end of file From 2cfb27cc03ac336df28e5c12ed148310bcd8c357 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Wed, 2 Dec 2020 05:54:01 +0000 Subject: [PATCH 16/24] equation.Expression.py v 0.0.1-alpha added corresponding .pyc to .gitignore --- .gitignore | 4 +- .../tra_analysis/equation/Expression.py | 68 +++++++++++++++++++ .../tra_analysis/equation/__init__.py | 1 + 3 files changed, 72 insertions(+), 1 deletion(-) create mode 100644 analysis-master/tra_analysis/equation/Expression.py create mode 100644 analysis-master/tra_analysis/equation/__init__.py diff --git a/.gitignore b/.gitignore index 2545560b..0c66ec45 100644 --- a/.gitignore +++ b/.gitignore @@ -38,4 +38,6 @@ analysis-master/tra_analysis/.ipynb_checkpoints .pytest_cache analysis-master/tra_analysis/metrics/__pycache__ analysis-master/dist -data-analysis/config/ \ No newline at end of file +data-analysis/config/ +analysis-master/tra_analysis/equation/__pycache__/Expression.cpython-38.pyc +analysis-master/tra_analysis/equation/__pycache__/__init__.cpython-38.pyc diff --git a/analysis-master/tra_analysis/equation/Expression.py b/analysis-master/tra_analysis/equation/Expression.py new file mode 100644 index 00000000..df2f73ff --- /dev/null +++ b/analysis-master/tra_analysis/equation/Expression.py @@ -0,0 +1,68 @@ +# Titan Robotics Team 2022: Expression submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import equation' +# setup: + +__version__ = "0.0.1-alpha" + +__changelog__ = """changelog: + 0.0.1-alpha: + - took items from equation.ipynb and ported here +""" + +__author__ = ( + "Arthur Lu ", +) + +import re +from decimal import Decimal +from functools import reduce + +class Expression: + + def __init__(self, string): + + self.string = string + + def add(self, string): + while(len(re.findall("[+]{1}[-]?", string)) != 0): + string = re.sub("[-]?\d+[.]?\d*[+]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x + y), [Decimal(i) for i in re.split("[+]{1}", re.search("[-]?\d+[.]?\d*[+]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) + return string + + def sub(self, string): + while(len(re.findall("\d+[.]?\d*[-]{1,2}\d+[.]?\d*", string)) != 0): + g = re.search("\d+[.]?\d*[-]{1,2}\d+[.]?\d*", string).group() + if(re.search("[-]{1,2}", g).group() == "-"): + r = re.sub("[-]{1}", "+-", g, 1) + string = re.sub(g, r, string, 1) + elif(re.search("[-]{1,2}", g).group() == "--"): + r = re.sub("[-]{2}", "+", g, 1) + string = re.sub(g, r, string, 1) + else: + pass + return string + + def mul(self, string): + while(len(re.findall("[*]{1}[-]?", string)) != 0): + string = re.sub("[-]?\d+[.]?\d*[*]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x * y), [Decimal(i) for i in re.split("[*]{1}", re.search("[-]?\d+[.]?\d*[*]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) + return string + + def div(self, string): + while(len(re.findall("[/]{1}[-]?", string)) != 0): + string = re.sub("[-]?\d+[.]?\d*[/]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x / y), [Decimal(i) for i in re.split("[/]{1}", re.search("[-]?\d+[.]?\d*[/]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) + return string + + def exp(self, string): + while(len(re.findall("[\^]{1}[-]?", string)) != 0): + string = re.sub("[-]?\d+[.]?\d*[\^]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x ** y), [Decimal(i) for i in re.split("[\^]{1}", re.search("[-]?\d+[.]?\d*[\^]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) + return string + + def evaluate(self): + string = self.string + string = self.exp(string) + string = self.div(string) + string = self.mul(string) + string = self.sub(string) + string = self.add(string) + return string \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/__init__.py b/analysis-master/tra_analysis/equation/__init__.py new file mode 100644 index 00000000..5948e0da --- /dev/null +++ b/analysis-master/tra_analysis/equation/__init__.py @@ -0,0 +1 @@ +from .Expression import Expression \ No newline at end of file From fe7865031bf7495caa686a2aaa7ab8e4a01a19a8 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Mon, 7 Dec 2020 23:33:59 +0000 Subject: [PATCH 17/24] parser.py v 0.0.2-alpha --- .gitignore | 2 + analysis-master/test_analysis.py | 50 +++++++- .../tra_analysis/equation/Expression.py | 68 ---------- .../tra_analysis/equation/__init__.py | 2 +- .../tra_analysis/equation/parser.py | 119 ++++++++++++++++++ analysis-master/tra_analysis/equation/py2.py | 21 ++++ 6 files changed, 192 insertions(+), 70 deletions(-) delete mode 100644 analysis-master/tra_analysis/equation/Expression.py create mode 100644 analysis-master/tra_analysis/equation/parser.py create mode 100644 analysis-master/tra_analysis/equation/py2.py diff --git a/.gitignore b/.gitignore index 0c66ec45..be2b2712 100644 --- a/.gitignore +++ b/.gitignore @@ -41,3 +41,5 @@ analysis-master/dist data-analysis/config/ analysis-master/tra_analysis/equation/__pycache__/Expression.cpython-38.pyc analysis-master/tra_analysis/equation/__pycache__/__init__.cpython-38.pyc +analysis-master/tra_analysis/equation/__pycache__/parser.cpython-38.pyc +analysis-master/tra_analysis/equation/__pycache__/py2.cpython-38.pyc diff --git a/analysis-master/test_analysis.py b/analysis-master/test_analysis.py index d281b5ef..da5286ab 100644 --- a/analysis-master/test_analysis.py +++ b/analysis-master/test_analysis.py @@ -1,6 +1,7 @@ from tra_analysis import analysis as an from tra_analysis import metrics from tra_analysis import fits +from tra_analysis.equation.parser import BNF def test_(): test_data_linear = [1, 3, 6, 7, 9] @@ -32,4 +33,51 @@ def test_(): assert all(a == b for a, b in zip(an.Sort().bubblesort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(an.Sort().cyclesort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(an.Sort().cocktailsort(test_data_scrambled), test_data_sorted)) - assert fits.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0) \ No newline at end of file + assert fits.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0) + + test_equation() + +def test_equation(): + + parser = BNF() + + assert parser.eval("9") == 9.0 + assert parser.eval("-9") == -9.0 + assert parser.eval("--9") == 9.0 + assert parser.eval("-E") == -2.718281828459045 + assert parser.eval("9 + 3 + 6") == 18.0 + assert parser.eval("9 + 3 / 11") == 9.272727272727273 + assert parser.eval("(9 + 3)") == 12.0 + assert parser.eval("(9+3) / 11") == 1.0909090909090908 + assert parser.eval("9 - 12 - 6") == -9.0 + assert parser.eval("9 - (12 - 6)") == 3.0 + assert parser.eval("2*3.14159") == 6.28318 + assert parser.eval("3.1415926535*3.1415926535 / 10") == 0.9869604400525172 + assert parser.eval("PI * PI / 10") == 0.9869604401089358 + assert parser.eval("PI*PI/10") == 0.9869604401089358 + assert parser.eval("PI^2") == 9.869604401089358 + assert parser.eval("round(PI^2)") == 10 + assert parser.eval("6.02E23 * 8.048") == 4.844896e+24 + assert parser.eval("e / 3") == 0.9060939428196817 + assert parser.eval("sin(PI/2)") == 1.0 + assert parser.eval("10+sin(PI/4)^2") == 10.5 + assert parser.eval("trunc(E)") == 2 + assert parser.eval("trunc(-E)") == -2 + assert parser.eval("round(E)") == 3 + assert parser.eval("round(-E)") == -3 + assert parser.eval("E^PI") == 23.140692632779263 + assert parser.eval("exp(0)") == 1.0 + assert parser.eval("exp(1)") == 2.718281828459045 + assert parser.eval("2^3^2") == 512.0 + assert parser.eval("(2^3)^2") == 64.0 + assert parser.eval("2^3+2") == 10.0 + assert parser.eval("2^3+5") == 13.0 + assert parser.eval("2^9") == 512.0 + assert parser.eval("sgn(-2)") == -1 + assert parser.eval("sgn(0)") == 0 + assert parser.eval("sgn(0.1)") == 1 + assert parser.eval("sgn(cos(PI/4))") == 1 + assert parser.eval("sgn(cos(PI/2))") == 0 + assert parser.eval("sgn(cos(PI*3/4))") == -1 + assert parser.eval("+(sgn(cos(PI/4)))") == 1 + assert parser.eval("-(sgn(cos(PI/4)))") == -1 \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/Expression.py b/analysis-master/tra_analysis/equation/Expression.py deleted file mode 100644 index df2f73ff..00000000 --- a/analysis-master/tra_analysis/equation/Expression.py +++ /dev/null @@ -1,68 +0,0 @@ -# Titan Robotics Team 2022: Expression submodule -# Written by Arthur Lu -# Notes: -# this should be imported as a python module using 'from tra_analysis import equation' -# setup: - -__version__ = "0.0.1-alpha" - -__changelog__ = """changelog: - 0.0.1-alpha: - - took items from equation.ipynb and ported here -""" - -__author__ = ( - "Arthur Lu ", -) - -import re -from decimal import Decimal -from functools import reduce - -class Expression: - - def __init__(self, string): - - self.string = string - - def add(self, string): - while(len(re.findall("[+]{1}[-]?", string)) != 0): - string = re.sub("[-]?\d+[.]?\d*[+]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x + y), [Decimal(i) for i in re.split("[+]{1}", re.search("[-]?\d+[.]?\d*[+]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) - return string - - def sub(self, string): - while(len(re.findall("\d+[.]?\d*[-]{1,2}\d+[.]?\d*", string)) != 0): - g = re.search("\d+[.]?\d*[-]{1,2}\d+[.]?\d*", string).group() - if(re.search("[-]{1,2}", g).group() == "-"): - r = re.sub("[-]{1}", "+-", g, 1) - string = re.sub(g, r, string, 1) - elif(re.search("[-]{1,2}", g).group() == "--"): - r = re.sub("[-]{2}", "+", g, 1) - string = re.sub(g, r, string, 1) - else: - pass - return string - - def mul(self, string): - while(len(re.findall("[*]{1}[-]?", string)) != 0): - string = re.sub("[-]?\d+[.]?\d*[*]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x * y), [Decimal(i) for i in re.split("[*]{1}", re.search("[-]?\d+[.]?\d*[*]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) - return string - - def div(self, string): - while(len(re.findall("[/]{1}[-]?", string)) != 0): - string = re.sub("[-]?\d+[.]?\d*[/]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x / y), [Decimal(i) for i in re.split("[/]{1}", re.search("[-]?\d+[.]?\d*[/]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) - return string - - def exp(self, string): - while(len(re.findall("[\^]{1}[-]?", string)) != 0): - string = re.sub("[-]?\d+[.]?\d*[\^]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x ** y), [Decimal(i) for i in re.split("[\^]{1}", re.search("[-]?\d+[.]?\d*[\^]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) - return string - - def evaluate(self): - string = self.string - string = self.exp(string) - string = self.div(string) - string = self.mul(string) - string = self.sub(string) - string = self.add(string) - return string \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/__init__.py b/analysis-master/tra_analysis/equation/__init__.py index 5948e0da..447703d3 100644 --- a/analysis-master/tra_analysis/equation/__init__.py +++ b/analysis-master/tra_analysis/equation/__init__.py @@ -1 +1 @@ -from .Expression import Expression \ No newline at end of file +from .parser import * \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/parser.py b/analysis-master/tra_analysis/equation/parser.py new file mode 100644 index 00000000..14e627fc --- /dev/null +++ b/analysis-master/tra_analysis/equation/parser.py @@ -0,0 +1,119 @@ +# Titan Robotics Team 2022: Expression submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import equation' +# adapted from https://github.com/pyparsing/pyparsing/blob/master/examples/fourFn.py +# setup: + +from __future__ import division + +__version__ = "0.0.2-alpha" + +__changelog__ = """changelog: + 0.0.2-alpha: + - wrote BNF using pyparsing and uses a BNF metasyntax + - renamed this submodule parser + 0.0.1-alpha: + - took items from equation.ipynb and ported here +""" + +__author__ = ( + "Arthur Lu ", +) + +from pyparsing import (Literal, CaselessLiteral, Word, Combine, Group, Optional, ZeroOrMore, Forward, nums, alphas, oneOf) +from . import py2 +import math +import operator + +class BNF(object): + + def pushFirst(self, strg, loc, toks): + self.exprStack.append(toks[0]) + + def pushUMinus(self, strg, loc, toks): + if toks and toks[0] == '-': + self.exprStack.append('unary -') + + def __init__(self): + """ + expop :: '^' + multop :: '*' | '/' + addop :: '+' | '-' + integer :: ['+' | '-'] '0'..'9'+ + atom :: PI | E | real | fn '(' expr ')' | '(' expr ')' + factor :: atom [ expop factor ]* + term :: factor [ multop factor ]* + expr :: term [ addop term ]* + """ + point = Literal(".") + e = CaselessLiteral("E") + fnumber = Combine(Word("+-" + nums, nums) + + Optional(point + Optional(Word(nums))) + + Optional(e + Word("+-" + nums, nums))) + ident = Word(alphas, alphas + nums + "_$") + plus = Literal("+") + minus = Literal("-") + mult = Literal("*") + div = Literal("/") + lpar = Literal("(").suppress() + rpar = Literal(")").suppress() + addop = plus | minus + multop = mult | div + expop = Literal("^") + pi = CaselessLiteral("PI") + expr = Forward() + atom = ((Optional(oneOf("- +")) + + (ident + lpar + expr + rpar | pi | e | fnumber).setParseAction(self.pushFirst)) + | Optional(oneOf("- +")) + Group(lpar + expr + rpar) + ).setParseAction(self.pushUMinus) + factor = Forward() + factor << atom + \ + ZeroOrMore((expop + factor).setParseAction(self.pushFirst)) + term = factor + \ + ZeroOrMore((multop + factor).setParseAction(self.pushFirst)) + expr << term + \ + ZeroOrMore((addop + term).setParseAction(self.pushFirst)) + + self.bnf = expr + + epsilon = 1e-12 + + self.opn = {"+": operator.add, + "-": operator.sub, + "*": operator.mul, + "/": operator.truediv, + "^": operator.pow} + self.fn = {"sin": math.sin, + "cos": math.cos, + "tan": math.tan, + "exp": math.exp, + "abs": abs, + "trunc": lambda a: int(a), + "round": round, + "sgn": lambda a: abs(a) > epsilon and py2.cmp(a, 0) or 0} + + def evaluateStack(self, s): + op = s.pop() + if op == 'unary -': + return -self.evaluateStack(s) + if op in "+-*/^": + op2 = self.evaluateStack(s) + op1 = self.evaluateStack(s) + return self.opn[op](op1, op2) + elif op == "PI": + return math.pi + elif op == "E": + return math.e + elif op in self.fn: + return self.fn[op](self.evaluateStack(s)) + elif op[0].isalpha(): + return 0 + else: + return float(op) + + def eval(self, num_string, parseAll=True): + self.exprStack = [] + results = self.bnf.parseString(num_string, parseAll) + val = self.evaluateStack(self.exprStack[:]) + return val \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/py2.py b/analysis-master/tra_analysis/equation/py2.py new file mode 100644 index 00000000..e9afa9a5 --- /dev/null +++ b/analysis-master/tra_analysis/equation/py2.py @@ -0,0 +1,21 @@ +# Titan Robotics Team 2022: py2 module +# Written by Arthur Lu +# Notes: +# this module should only be used internally, contains old python 2.X functions that have been removed. +# setup: + +from __future__ import division + +__version__ = "1.0.0" + +__changelog__ = """changelog: + 1.0.0: + - added cmp function +""" + +__author__ = ( + "Arthur Lu ", +) + +def cmp(a, b): + return (a > b) - (a < b) \ No newline at end of file From 964e042e92239776ecbb92e26b382f3a3392e643 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Tue, 8 Dec 2020 04:42:15 +0000 Subject: [PATCH 18/24] added pyparsing to requirements.txt --- analysis-master/requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/analysis-master/requirements.txt b/analysis-master/requirements.txt index 33435d14..3d106798 100644 --- a/analysis-master/requirements.txt +++ b/analysis-master/requirements.txt @@ -2,4 +2,5 @@ numpy scipy scikit-learn six -matplotlib \ No newline at end of file +matplotlib +pyparsing \ No newline at end of file From c451b1ebb9e4a76ab760822ba985ed456796fa4e Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Wed, 9 Dec 2020 01:30:21 +0000 Subject: [PATCH 19/24] parser v 0.0.4-alpha --- .gitignore | 6 +-- .../tra_analysis/equation/Expression.py | 38 ++++++++++++++ .../tra_analysis/equation/__init__.py | 2 +- .../equation/{parser.py => parser/BNF.py} | 22 -------- .../equation/parser/RegexInplaceParser.py | 51 +++++++++++++++++++ .../tra_analysis/equation/parser/__init__.py | 32 ++++++++++++ .../tra_analysis/equation/{ => parser}/py2.py | 0 7 files changed, 124 insertions(+), 27 deletions(-) create mode 100644 analysis-master/tra_analysis/equation/Expression.py rename analysis-master/tra_analysis/equation/{parser.py => parser/BNF.py} (82%) create mode 100644 analysis-master/tra_analysis/equation/parser/RegexInplaceParser.py create mode 100644 analysis-master/tra_analysis/equation/parser/__init__.py rename analysis-master/tra_analysis/equation/{ => parser}/py2.py (100%) diff --git a/.gitignore b/.gitignore index be2b2712..1fd768eb 100644 --- a/.gitignore +++ b/.gitignore @@ -39,7 +39,5 @@ analysis-master/tra_analysis/.ipynb_checkpoints analysis-master/tra_analysis/metrics/__pycache__ analysis-master/dist data-analysis/config/ -analysis-master/tra_analysis/equation/__pycache__/Expression.cpython-38.pyc -analysis-master/tra_analysis/equation/__pycache__/__init__.cpython-38.pyc -analysis-master/tra_analysis/equation/__pycache__/parser.cpython-38.pyc -analysis-master/tra_analysis/equation/__pycache__/py2.cpython-38.pyc +analysis-master/tra_analysis/equation/__pycache__/* +analysis-master/tra_analysis/equation/parser/__pycache__/* diff --git a/analysis-master/tra_analysis/equation/Expression.py b/analysis-master/tra_analysis/equation/Expression.py new file mode 100644 index 00000000..df659557 --- /dev/null +++ b/analysis-master/tra_analysis/equation/Expression.py @@ -0,0 +1,38 @@ +# Titan Robotics Team 2022: Expression submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis.Equation import Expression' +# adapted from https://github.com/pyparsing/pyparsing/blob/master/examples/fourFn.py +# setup: + +__version__ = "0.0.1-alpha" + +__changelog__ = """changelog: + 0.0.1-alpha: +""" + +__author__ = ( + "Arthur Lu ", +) + +import re +from .parser import BNF + +class Expression(): + + expression = None + protected = list(BNF().fn.keys()) + + def __init__(self, s): + if(self.validate(s)): + self.expression = s + else: + pass + + def validate(self, s): + + return true + + def substitute(self, var, value): + + pass \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/__init__.py b/analysis-master/tra_analysis/equation/__init__.py index 447703d3..5948e0da 100644 --- a/analysis-master/tra_analysis/equation/__init__.py +++ b/analysis-master/tra_analysis/equation/__init__.py @@ -1 +1 @@ -from .parser import * \ No newline at end of file +from .Expression import Expression \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/parser.py b/analysis-master/tra_analysis/equation/parser/BNF.py similarity index 82% rename from analysis-master/tra_analysis/equation/parser.py rename to analysis-master/tra_analysis/equation/parser/BNF.py index 14e627fc..97b2bfb4 100644 --- a/analysis-master/tra_analysis/equation/parser.py +++ b/analysis-master/tra_analysis/equation/parser/BNF.py @@ -1,26 +1,4 @@ -# Titan Robotics Team 2022: Expression submodule -# Written by Arthur Lu -# Notes: -# this should be imported as a python module using 'from tra_analysis import equation' -# adapted from https://github.com/pyparsing/pyparsing/blob/master/examples/fourFn.py -# setup: - from __future__ import division - -__version__ = "0.0.2-alpha" - -__changelog__ = """changelog: - 0.0.2-alpha: - - wrote BNF using pyparsing and uses a BNF metasyntax - - renamed this submodule parser - 0.0.1-alpha: - - took items from equation.ipynb and ported here -""" - -__author__ = ( - "Arthur Lu ", -) - from pyparsing import (Literal, CaselessLiteral, Word, Combine, Group, Optional, ZeroOrMore, Forward, nums, alphas, oneOf) from . import py2 import math diff --git a/analysis-master/tra_analysis/equation/parser/RegexInplaceParser.py b/analysis-master/tra_analysis/equation/parser/RegexInplaceParser.py new file mode 100644 index 00000000..d8886878 --- /dev/null +++ b/analysis-master/tra_analysis/equation/parser/RegexInplaceParser.py @@ -0,0 +1,51 @@ +import re +from decimal import Decimal +from functools import reduce + +class RegexInplaceParser(object): + + def __init__(self, string): + + self.string = string + + def add(self, string): + while(len(re.findall("[+]{1}[-]?", string)) != 0): + string = re.sub("[-]?\d+[.]?\d*[+]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x + y), [Decimal(i) for i in re.split("[+]{1}", re.search("[-]?\d+[.]?\d*[+]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) + return string + + def sub(self, string): + while(len(re.findall("\d+[.]?\d*[-]{1,2}\d+[.]?\d*", string)) != 0): + g = re.search("\d+[.]?\d*[-]{1,2}\d+[.]?\d*", string).group() + if(re.search("[-]{1,2}", g).group() == "-"): + r = re.sub("[-]{1}", "+-", g, 1) + string = re.sub(g, r, string, 1) + elif(re.search("[-]{1,2}", g).group() == "--"): + r = re.sub("[-]{2}", "+", g, 1) + string = re.sub(g, r, string, 1) + else: + pass + return string + + def mul(self, string): + while(len(re.findall("[*]{1}[-]?", string)) != 0): + string = re.sub("[-]?\d+[.]?\d*[*]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x * y), [Decimal(i) for i in re.split("[*]{1}", re.search("[-]?\d+[.]?\d*[*]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) + return string + + def div(self, string): + while(len(re.findall("[/]{1}[-]?", string)) != 0): + string = re.sub("[-]?\d+[.]?\d*[/]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x / y), [Decimal(i) for i in re.split("[/]{1}", re.search("[-]?\d+[.]?\d*[/]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) + return string + + def exp(self, string): + while(len(re.findall("[\^]{1}[-]?", string)) != 0): + string = re.sub("[-]?\d+[.]?\d*[\^]{1}[-]?\d+[.]?\d*", str("%f" % reduce((lambda x, y: x ** y), [Decimal(i) for i in re.split("[\^]{1}", re.search("[-]?\d+[.]?\d*[\^]{1}[-]?\d+[.]?\d*", string).group())])), string, 1) + return string + + def evaluate(self): + string = self.string + string = self.exp(string) + string = self.div(string) + string = self.mul(string) + string = self.sub(string) + string = self.add(string) + return string \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/parser/__init__.py b/analysis-master/tra_analysis/equation/parser/__init__.py new file mode 100644 index 00000000..17b284ed --- /dev/null +++ b/analysis-master/tra_analysis/equation/parser/__init__.py @@ -0,0 +1,32 @@ +# Titan Robotics Team 2022: Expression submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis.Equation import parser' +# adapted from https://github.com/pyparsing/pyparsing/blob/master/examples/fourFn.py +# setup: + +__version__ = "0.0.4-alpha" + +__changelog__ = """changelog: + 0.0.4-alpha: + - moved individual parsers to their own filespar + 0.0.3-alpha: + - readded old regex based parser as RegexInplaceParser + 0.0.2-alpha: + - wrote BNF using pyparsing and uses a BNF metasyntax + - renamed this submodule parser + 0.0.1-alpha: + - took items from equation.ipynb and ported here +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = { + "BNF", + "RegexInplaceParser" +} + +from .BNF import BNF as BNF +from .RegexInplaceParser import RegexInplaceParser as RegexInplaceParser \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/py2.py b/analysis-master/tra_analysis/equation/parser/py2.py similarity index 100% rename from analysis-master/tra_analysis/equation/py2.py rename to analysis-master/tra_analysis/equation/parser/py2.py From 2f280bedf674d7df0cd543da232d18716832bceb Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Wed, 9 Dec 2020 03:56:22 +0000 Subject: [PATCH 20/24] Equation v 0.0.1-alpha --- .gitignore | 1 + .../tra_analysis/equation/Expression.py | 35 +- .../tra_analysis/equation/__init__.py | 21 + .../tra_analysis/equation/parser/Hybrid.py | 521 ++++++++++++++++++ .../parser/Hybrid_Utils/ExpressionCore.py | 237 ++++++++ .../equation/parser/Hybrid_Utils/__init__.py | 2 + .../parser/Hybrid_Utils/equation_base.py | 106 ++++ .../equation/parser/Hybrid_Utils/similar.py | 49 ++ .../tra_analysis/equation/parser/__init__.py | 10 +- .../tra_analysis/equation/parser/py2.py | 2 +- 10 files changed, 961 insertions(+), 23 deletions(-) create mode 100644 analysis-master/tra_analysis/equation/parser/Hybrid.py create mode 100644 analysis-master/tra_analysis/equation/parser/Hybrid_Utils/ExpressionCore.py create mode 100644 analysis-master/tra_analysis/equation/parser/Hybrid_Utils/__init__.py create mode 100644 analysis-master/tra_analysis/equation/parser/Hybrid_Utils/equation_base.py create mode 100644 analysis-master/tra_analysis/equation/parser/Hybrid_Utils/similar.py diff --git a/.gitignore b/.gitignore index 1fd768eb..d0bd9faa 100644 --- a/.gitignore +++ b/.gitignore @@ -41,3 +41,4 @@ analysis-master/dist data-analysis/config/ analysis-master/tra_analysis/equation/__pycache__/* analysis-master/tra_analysis/equation/parser/__pycache__/* +analysis-master/tra_analysis/equation/parser/Hybrid_Utils/__pycache__/* diff --git a/analysis-master/tra_analysis/equation/Expression.py b/analysis-master/tra_analysis/equation/Expression.py index df659557..4ab2767a 100644 --- a/analysis-master/tra_analysis/equation/Expression.py +++ b/analysis-master/tra_analysis/equation/Expression.py @@ -2,37 +2,36 @@ # Written by Arthur Lu # Notes: # this should be imported as a python module using 'from tra_analysis.Equation import Expression' -# adapted from https://github.com/pyparsing/pyparsing/blob/master/examples/fourFn.py +# TODO: +# - add option to pick parser backend +# - fix unit tests # setup: __version__ = "0.0.1-alpha" __changelog__ = """changelog: 0.0.1-alpha: + - used the HybridExpressionParser as backend for Expression """ __author__ = ( "Arthur Lu ", ) -import re -from .parser import BNF +__all__ = { + "Expression" +} -class Expression(): +import re +from .parser import BNF, RegexInplaceParser, HybridExpressionParser, Core, equation_base + +class Expression(HybridExpressionParser): expression = None - protected = list(BNF().fn.keys()) + core = None - def __init__(self, s): - if(self.validate(s)): - self.expression = s - else: - pass - - def validate(self, s): - - return true - - def substitute(self, var, value): - - pass \ No newline at end of file + def __init__(self,expression,argorder=[],*args,**kwargs): + self.core = Core() + equation_base.equation_extend(self.core) + self.core.recalculateFMatch() + super().__init__(self.core, expression, argorder=[],*args,**kwargs) \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/__init__.py b/analysis-master/tra_analysis/equation/__init__.py index 5948e0da..8bfe8316 100644 --- a/analysis-master/tra_analysis/equation/__init__.py +++ b/analysis-master/tra_analysis/equation/__init__.py @@ -1 +1,22 @@ +# Titan Robotics Team 2022: Expression submodule +# Written by Arthur Lu +# Notes: +# this should be imported as a python module using 'from tra_analysis import Equation' +# setup: + +__version__ = "0.0.1-alpha" + +__changelog__ = """changelog: + 0.0.1-alpha: + - made first prototype of Expression +""" + +__author__ = ( + "Arthur Lu ", +) + +__all__ = { + "Expression" +} + from .Expression import Expression \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/parser/Hybrid.py b/analysis-master/tra_analysis/equation/parser/Hybrid.py new file mode 100644 index 00000000..4f41c7b1 --- /dev/null +++ b/analysis-master/tra_analysis/equation/parser/Hybrid.py @@ -0,0 +1,521 @@ +from .Hybrid_Utils import Core, ExpressionFunction, ExpressionVariable, ExpressionValue +import sys + +if sys.version_info >= (3,): + xrange = range + basestring = str + +class HybridExpressionParser(object): + + def __init__(self,core,expression,argorder=[],*args,**kwargs): + super(HybridExpressionParser,self).__init__(*args,**kwargs) + if isinstance(expression,type(self)): # clone the object + self.core = core + self.__args = list(expression.__args) + self.__vars = dict(expression.__vars) # intenral array of preset variables + self.__argsused = set(expression.__argsused) + self.__expr = list(expression.__expr) + self.variables = {} # call variables + else: + self.__expression = expression + self.__args = argorder; + self.__vars = {} # intenral array of preset variables + self.__argsused = set() + self.__expr = [] # compiled equation tokens + self.variables = {} # call variables + self.__compile() + del self.__expression + + def __getitem__(self, name): + if name in self.__argsused: + if name in self.__vars: + return self.__vars[name] + else: + return None + else: + raise KeyError(name) + + def __setitem__(self,name,value): + + if name in self.__argsused: + self.__vars[name] = value + else: + raise KeyError(name) + + def __delitem__(self,name): + + if name in self.__argsused: + if name in self.__vars: + del self.__vars[name] + else: + raise KeyError(name) + + def __contains__(self, name): + + return name in self.__argsused + + def __call__(self,*args,**kwargs): + + if len(self.__expr) == 0: + return None + self.variables = {} + self.variables.update(self.core.constants) + self.variables.update(self.__vars) + if len(args) > len(self.__args): + raise TypeError("<{0:s}.{1:s}({2:s}) object at {3:0=#10x}>() takes at most {4:d} arguments ({5:d} given)".format( + type(self).__module__,type(self).__name__,repr(self),id(self),len(self.__args),len(args))) + for i in xrange(len(args)): + if i < len(self.__args): + if self.__args[i] in kwargs: + raise TypeError("<{0:s}.{1:s}({2:s}) object at {3:0=#10x}>() got multiple values for keyword argument '{4:s}'".format( + type(self).__module__,type(self).__name__,repr(self),id(self),self.__args[i])) + self.variables[self.__args[i]] = args[i] + self.variables.update(kwargs) + for arg in self.__argsused: + if arg not in self.variables: + min_args = len(self.__argsused - (set(self.__vars.keys()) | set(self.core.constants.keys()))) + raise TypeError("<{0:s}.{1:s}({2:s}) object at {3:0=#10x}>() takes at least {4:d} arguments ({5:d} given) '{6:s}' not defined".format( + type(self).__module__,type(self).__name__,repr(self),id(self),min_args,len(args)+len(kwargs),arg)) + expr = self.__expr[::-1] + args = [] + while len(expr) > 0: + t = expr.pop() + r = t(args,self) + args.append(r) + if len(args) > 1: + return args + else: + return args[0] + + def __next(self,__expect_op): + if __expect_op: + m = self.core.gematch.match(self.__expression) + if m != None: + self.__expression = self.__expression[m.end():] + g = m.groups() + return g[0],'CLOSE' + m = self.core.smatch.match(self.__expression) + if m != None: + self.__expression = self.__expression[m.end():] + return ",",'SEP' + m = self.core.omatch.match(self.__expression) + if m != None: + self.__expression = self.__expression[m.end():] + g = m.groups() + return g[0],'OP' + else: + m = self.core.gsmatch.match(self.__expression) + if m != None: + self.__expression = self.__expression[m.end():] + g = m.groups() + return g[0],'OPEN' + m = self.core.vmatch.match(self.__expression) + if m != None: + self.__expression = self.__expression[m.end():] + g = m.groupdict(0) + if g['dec']: + if g["ivalue"]: + return complex(int(g["rsign"]+"1")*float(g["rvalue"])*10**int(g["rexpoent"]),int(g["isign"]+"1")*float(g["ivalue"])*10**int(g["iexpoent"])),'VALUE' + elif g["rexpoent"] or g["rvalue"].find('.')>=0: + return int(g["rsign"]+"1")*float(g["rvalue"])*10**int(g["rexpoent"]),'VALUE' + else: + return int(g["rsign"]+"1")*int(g["rvalue"]),'VALUE' + elif g["hex"]: + return int(g["hexsign"]+"1")*int(g["hexvalue"],16),'VALUE' + elif g["oct"]: + return int(g["octsign"]+"1")*int(g["octvalue"],8),'VALUE' + elif g["bin"]: + return int(g["binsign"]+"1")*int(g["binvalue"],2),'VALUE' + else: + raise NotImplemented("'{0:s}' Values Not Implemented Yet".format(m.string)) + m = self.core.nmatch.match(self.__expression) + if m != None: + self.__expression = self.__expression[m.end():] + g = m.groups() + return g[0],'NAME' + m = self.core.fmatch.match(self.__expression) + if m != None: + self.__expression = self.__expression[m.end():] + g = m.groups() + return g[0],'FUNC' + m = self.core.umatch.match(self.__expression) + if m != None: + self.__expression = self.__expression[m.end():] + g = m.groups() + return g[0],'UNARY' + return None + + def show(self): + """Show RPN tokens + + This will print out the internal token list (RPN) of the expression + one token perline. + """ + for expr in self.__expr: + print(expr) + + def __str__(self): + """str(fn) + + Generates a Printable version of the Expression + + Returns + ------- + str + Latex String respresation of the Expression, suitable for rendering the equation + """ + expr = self.__expr[::-1] + if len(expr) == 0: + return "" + args = []; + while len(expr) > 0: + t = expr.pop() + r = t.toStr(args,self) + args.append(r) + if len(args) > 1: + return args + else: + return args[0] + + def __repr__(self): + """repr(fn) + + Generates a String that correctrly respresents the equation + + Returns + ------- + str + Convert the Expression to a String that passed to the constructor, will constuct + an identical equation object (in terms of sequence of tokens, and token type/value) + """ + expr = self.__expr[::-1] + if len(expr) == 0: + return "" + args = []; + while len(expr) > 0: + t = expr.pop() + r = t.toRepr(args,self) + args.append(r) + if len(args) > 1: + return args + else: + return args[0] + + def __iter__(self): + return iter(self.__argsused) + + def __lt__(self, other): + if isinstance(other, Expression): + return repr(self) < repr(other) + else: + raise TypeError("{0:s} is not an {1:s} Object, and can't be compared to an Expression Object".format(repr(other), type(other))) + + def __eq__(self, other): + if isinstance(other, Expression): + return repr(self) == repr(other) + else: + raise TypeError("{0:s} is not an {1:s} Object, and can't be compared to an Expression Object".format(repr(other), type(other))) + + def __combine(self,other,op): + if op not in self.core.ops or not isinstance(other,(int,float,complex,type(self),basestring)): + return NotImplemented + else: + obj = type(self)(self) + if isinstance(other,(int,float,complex)): + obj.__expr.append(ExpressionValue(other)) + else: + if isinstance(other,basestring): + try: + other = type(self)(other) + except: + raise SyntaxError("Can't Convert string, \"{0:s}\" to an Expression Object".format(other)) + obj.__expr += other.__expr + obj.__argsused |= other.__argsused + for v in other.__args: + if v not in obj.__args: + obj.__args.append(v) + for k,v in other.__vars.items(): + if k not in obj.__vars: + obj.__vars[k] = v + elif v != obj.__vars[k]: + raise RuntimeError("Predifined Variable Conflict in '{0:s}' two differing values defined".format(k)) + fn = self.core.ops[op] + obj.__expr.append(ExpressionFunction(fn['func'],fn['args'],fn['str'],fn['latex'],op,False)) + return obj + + def __rcombine(self,other,op): + if op not in self.core.ops or not isinstance(other,(int,float,complex,type(self),basestring)): + return NotImplemented + else: + obj = type(self)(self) + if isinstance(other,(int,float,complex)): + obj.__expr.insert(0,ExpressionValue(other)) + else: + if isinstance(other,basestring): + try: + other = type(self)(other) + except: + raise SyntaxError("Can't Convert string, \"{0:s}\" to an Expression Object".format(other)) + obj.__expr = other.__expr + self.__expr + obj.__argsused = other.__argsused | self.__expr + __args = other.__args + for v in obj.__args: + if v not in __args: + __args.append(v) + obj.__args = __args + for k,v in other.__vars.items(): + if k not in obj.__vars: + obj.__vars[k] = v + elif v != obj.__vars[k]: + raise RuntimeError("Predifined Variable Conflict in '{0:s}' two differing values defined".format(k)) + fn = self.core.ops[op] + obj.__expr.append(ExpressionFunction(fn['func'],fn['args'],fn['str'],fn['latex'],op,False)) + return obj + + def __icombine(self,other,op): + if op not in self.core.ops or not isinstance(other,(int,float,complex,type(self),basestring)): + return NotImplemented + else: + obj = self + if isinstance(other,(int,float,complex)): + obj.__expr.append(ExpressionValue(other)) + else: + if isinstance(other,basestring): + try: + other = type(self)(other) + except: + raise SyntaxError("Can't Convert string, \"{0:s}\" to an Expression Object".format(other)) + obj.__expr += other.__expr + obj.__argsused |= other.__argsused + for v in other.__args: + if v not in obj.__args: + obj.__args.append(v) + for k,v in other.__vars.items(): + if k not in obj.__vars: + obj.__vars[k] = v + elif v != obj.__vars[k]: + raise RuntimeError("Predifined Variable Conflict in '{0:s}' two differing values defined".format(k)) + fn = self.core.ops[op] + obj.__expr.append(ExpressionFunction(fn['func'],fn['args'],fn['str'],fn['latex'],op,False)) + return obj + + def __apply(self,op): + fn = self.core.unary_ops[op] + obj = type(self)(self) + obj.__expr.append(ExpressionFunction(fn['func'],1,fn['str'],fn['latex'],op,False)) + return obj + + def __applycall(self,op): + fn = self.core.functions[op] + if 1 not in fn['args'] or '*' not in fn['args']: + raise RuntimeError("Can't Apply {0:s} function, dosen't accept only 1 argument".format(op)) + obj = type(self)(self) + obj.__expr.append(ExpressionFunction(fn['func'],1,fn['str'],fn['latex'],op,False)) + return obj + + def __add__(self,other): + return self.__combine(other,'+') + + def __sub__(self,other): + return self.__combine(other,'-') + + def __mul__(self,other): + return self.__combine(other,'*') + + def __div__(self,other): + return self.__combine(other,'/') + + def __truediv__(self,other): + return self.__combine(other,'/') + + def __pow__(self,other): + return self.__combine(other,'^') + + def __mod__(self,other): + return self.__combine(other,'%') + + def __and__(self,other): + return self.__combine(other,'&') + + def __or__(self,other): + return self.__combine(other,'|') + + def __xor__(self,other): + return self.__combine(other,'') + + def __radd__(self,other): + return self.__rcombine(other,'+') + + def __rsub__(self,other): + return self.__rcombine(other,'-') + + def __rmul__(self,other): + return self.__rcombine(other,'*') + + def __rdiv__(self,other): + return self.__rcombine(other,'/') + + def __rtruediv__(self,other): + return self.__rcombine(other,'/') + + def __rpow__(self,other): + return self.__rcombine(other,'^') + + def __rmod__(self,other): + return self.__rcombine(other,'%') + + def __rand__(self,other): + return self.__rcombine(other,'&') + + def __ror__(self,other): + return self.__rcombine(other,'|') + + def __rxor__(self,other): + return self.__rcombine(other,'') + + def __iadd__(self,other): + return self.__icombine(other,'+') + + def __isub__(self,other): + return self.__icombine(other,'-') + + def __imul__(self,other): + return self.__icombine(other,'*') + + def __idiv__(self,other): + return self.__icombine(other,'/') + + def __itruediv__(self,other): + return self.__icombine(other,'/') + + def __ipow__(self,other): + return self.__icombine(other,'^') + + def __imod__(self,other): + return self.__icombine(other,'%') + + def __iand__(self,other): + return self.__icombine(other,'&') + + def __ior__(self,other): + return self.__icombine(other,'|') + + def __ixor__(self,other): + return self.__icombine(other,'') + + def __neg__(self): + return self.__apply('-') + + def __invert__(self): + return self.__apply('!') + + def __abs__(self): + return self.__applycall('abs') + + def __getfunction(self,op): + if op[1] == 'FUNC': + fn = self.core.functions[op[0]] + fn['type'] = 'FUNC' + elif op[1] == 'UNARY': + fn = self.core.unary_ops[op[0]] + fn['type'] = 'UNARY' + fn['args'] = 1 + elif op[1] == 'OP': + fn = self.core.ops[op[0]] + fn['type'] = 'OP' + return fn + + def __compile(self): + self.__expr = [] + stack = [] + argc = [] + __expect_op = False + v = self.__next(__expect_op) + while v != None: + if not __expect_op and v[1] == "OPEN": + stack.append(v) + __expect_op = False + elif __expect_op and v[1] == "CLOSE": + op = stack.pop() + while op[1] != "OPEN": + fs = self.__getfunction(op) + self.__expr.append(ExpressionFunction(fs['func'],fs['args'],fs['str'],fs['latex'],op[0],False)) + op = stack.pop() + if len(stack) > 0 and stack[-1][0] in self.core.functions: + op = stack.pop() + fs = self.core.functions[op[0]] + args = argc.pop() + if fs['args'] != '+' and (args != fs['args'] and args not in fs['args']): + raise SyntaxError("Invalid number of arguments for {0:s} function".format(op[0])) + self.__expr.append(ExpressionFunction(fs['func'],args,fs['str'],fs['latex'],op[0],True)) + __expect_op = True + elif __expect_op and v[0] == ",": + argc[-1] += 1 + op = stack.pop() + while op[1] != "OPEN": + fs = self.__getfunction(op) + self.__expr.append(ExpressionFunction(fs['func'],fs['args'],fs['str'],fs['latex'],op[0],False)) + op = stack.pop() + stack.append(op) + __expect_op = False + elif __expect_op and v[0] in self.core.ops: + fn = self.core.ops[v[0]] + if len(stack) == 0: + stack.append(v) + __expect_op = False + v = self.__next(__expect_op) + continue + op = stack.pop() + if op[0] == "(": + stack.append(op) + stack.append(v) + __expect_op = False + v = self.__next(__expect_op) + continue + fs = self.__getfunction(op) + while True: + if (fn['prec'] >= fs['prec']): + self.__expr.append(ExpressionFunction(fs['func'],fs['args'],fs['str'],fs['latex'],op[0],False)) + if len(stack) == 0: + stack.append(v) + break + op = stack.pop() + if op[0] == "(": + stack.append(op) + stack.append(v) + break + fs = self.__getfunction(op) + else: + stack.append(op) + stack.append(v) + break + __expect_op = False + elif not __expect_op and v[0] in self.core.unary_ops: + fn = self.core.unary_ops[v[0]] + stack.append(v) + __expect_op = False + elif not __expect_op and v[0] in self.core.functions: + stack.append(v) + argc.append(1) + __expect_op = False + elif not __expect_op and v[1] == 'NAME': + self.__argsused.add(v[0]) + if v[0] not in self.__args: + self.__args.append(v[0]) + self.__expr.append(ExpressionVariable(v[0])) + __expect_op = True + elif not __expect_op and v[1] == 'VALUE': + self.__expr.append(ExpressionValue(v[0])) + __expect_op = True + else: + raise SyntaxError("Invalid Token \"{0:s}\" in Expression, Expected {1:s}".format(v,"Op" if __expect_op else "Value")) + v = self.__next(__expect_op) + if len(stack) > 0: + op = stack.pop() + while op != "(": + fs = self.__getfunction(op) + self.__expr.append(ExpressionFunction(fs['func'],fs['args'],fs['str'],fs['latex'],op[0],False)) + if len(stack) > 0: + op = stack.pop() + else: + break \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/ExpressionCore.py b/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/ExpressionCore.py new file mode 100644 index 00000000..be4d4e0d --- /dev/null +++ b/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/ExpressionCore.py @@ -0,0 +1,237 @@ +import math +import sys +import re + +if sys.version_info >= (3,): + xrange = range + basestring = str + +class ExpressionObject(object): + def __init__(self,*args,**kwargs): + super(ExpressionObject,self).__init__(*args,**kwargs) + + def toStr(self,args,expression): + return "" + + def toRepr(self,args,expression): + return "" + + def __call__(self,args,expression): + pass + +class ExpressionValue(ExpressionObject): + def __init__(self,value,*args,**kwargs): + super(ExpressionValue,self).__init__(*args,**kwargs) + self.value = value + + def toStr(self,args,expression): + if (isinstance(self.value,complex)): + V = [self.value.real,self.value.imag] + E = [0,0] + B = [0,0] + out = ["",""] + for i in xrange(2): + if V[i] == 0: + E[i] = 0 + B[i] = 0 + else: + E[i] = int(math.floor(math.log10(abs(V[i])))) + B[i] = V[i]*10**-E[i] + if E[i] in [0,1,2,3] and str(V[i])[-2:] == ".0": + B[i] = int(V[i]) + E[i] = 0 + if E[i] in [-1,-2] and len(str(V[i])) <= 7: + B[i] = V[i] + E[i] = 0 + if i == 1: + fmt = "{{0:+{0:s}}}" + else: + fmt = "{{0:-{0:s}}}" + if type(B[i]) == int: + out[i] += fmt.format('d').format(B[i]) + else: + out[i] += fmt.format('.5f').format(B[i]).rstrip("0.") + if i == 1: + out[i] += "\\imath" + if E[i] != 0: + out[i] += "\\times10^{{{0:d}}}".format(E[i]) + return "\\left(" + ''.join(out) + "\\right)" + elif (isinstance(self.value,float)): + V = self.value + E = 0 + B = 0 + out = "" + if V == 0: + E = 0 + B = 0 + else: + E = int(math.floor(math.log10(abs(V)))) + B = V*10**-E + if E in [0,1,2,3] and str(V)[-2:] == ".0": + B = int(V) + E = 0 + if E in [-1,-2] and len(str(V)) <= 7: + B = V + E = 0 + if type(B) == int: + out += "{0:-d}".format(B) + else: + out += "{0:-.5f}".format(B).rstrip("0.") + if E != 0: + out += "\\times10^{{{0:d}}}".format(E) + return "\\left(" + out + "\\right)" + else: + return out + else: + return str(self.value) + + def toRepr(self,args,expression): + return str(self.value) + + def __call__(self,args,expression): + return self.value + + def __repr__(self): + return "<{0:s}.{1:s}({2:s}) object at {3:0=#10x}>".format(type(self).__module__,type(self).__name__,str(self.value),id(self)) + +class ExpressionFunction(ExpressionObject): + def __init__(self,function,nargs,form,display,id,isfunc,*args,**kwargs): + super(ExpressionFunction,self).__init__(*args,**kwargs) + self.function = function + self.nargs = nargs + self.form = form + self.display = display + self.id = id + self.isfunc = isfunc + + def toStr(self,args,expression): + params = [] + for i in xrange(self.nargs): + params.append(args.pop()) + if self.isfunc: + return str(self.display.format(','.join(params[::-1]))) + else: + return str(self.display.format(*params[::-1])) + + def toRepr(self,args,expression): + params = [] + for i in xrange(self.nargs): + params.append(args.pop()) + if self.isfunc: + return str(self.form.format(','.join(params[::-1]))) + else: + return str(self.form.format(*params[::-1])) + + def __call__(self,args,expression): + params = [] + for i in xrange(self.nargs): + params.append(args.pop()) + return self.function(*params[::-1]) + + def __repr__(self): + return "<{0:s}.{1:s}({2:s},{3:d}) object at {4:0=#10x}>".format(type(self).__module__,type(self).__name__,str(self.id),self.nargs,id(self)) + +class ExpressionVariable(ExpressionObject): + def __init__(self,name,*args,**kwargs): + super(ExpressionVariable,self).__init__(*args,**kwargs) + self.name = name + + def toStr(self,args,expression): + return str(self.name) + + def toRepr(self,args,expression): + return str(self.name) + + def __call__(self,args,expression): + if self.name in expression.variables: + return expression.variables[self.name] + else: + return 0 # Default variables to return 0 + + def __repr__(self): + return "<{0:s}.{1:s}({2:s}) object at {3:0=#10x}>".format(type(self).__module__,type(self).__name__,str(self.name),id(self)) + +class Core(): + + constants = {} + unary_ops = {} + ops = {} + functions = {} + smatch = re.compile("\s*,") + vmatch = re.compile("\s*" + "(?:" + "(?P" + "(?P[+-]?)" + "\s*0o" + "(?P[0-7]+)" + ")|(?P" + "(?P[+-]?)" + "\s*0x" + "(?P[0-9a-fA-F]+)" + ")|(?P" + "(?P[+-]?)" + "\s*0b" + "(?P[01]+)" + ")|(?P" + "(?P[+-]?)" + "\s*" + "(?P(?:\d+\.\d+|\d+\.|\.\d+|\d+))" + "(?:" + "[Ee]" + "(?P[+-]?\d+)" + ")?" + "(?:" + "\s*" + "(?P(?(rvalue)\+|))?" + "\s*" + "(?P(?(rvalue)(?(sep)[+-]?|[+-])|[+-]?)?)" + "\s*" + "(?P(?:\d+\.\d+|\d+\.|\.\d+|\d+))" + "(?:" + "[Ee]" + "(?P[+-]?\d+)" + ")?" + "[ij]" + ")?" + ")" + ")") + nmatch = re.compile("\s*([a-zA-Z_][a-zA-Z0-9_]*)") + gsmatch = re.compile('\s*(\()') + gematch = re.compile('\s*(\))') + + def recalculateFMatch(self): + + fks = sorted(self.functions.keys(), key=len, reverse=True) + oks = sorted(self.ops.keys(), key=len, reverse=True) + uks = sorted(self.unary_ops.keys(), key=len, reverse=True) + self.fmatch = re.compile('\s*(' + '|'.join(map(re.escape,fks)) + ')') + self.omatch = re.compile('\s*(' + '|'.join(map(re.escape,oks)) + ')') + self.umatch = re.compile('\s*(' + '|'.join(map(re.escape,uks)) + ')') + + def addFn(self,id,str,latex,args,func): + self.functions[id] = { + 'str': str, + 'latex': latex, + 'args': args, + 'func': func} + + def addOp(self,id,str,latex,single,prec,func): + if single: + raise RuntimeError("Single Ops Not Yet Supported") + self.ops[id] = { + 'str': str, + 'latex': latex, + 'args': 2, + 'prec': prec, + 'func': func} + + def addUnaryOp(self,id,str,latex,func): + self.unary_ops[id] = { + 'str': str, + 'latex': latex, + 'args': 1, + 'prec': 0, + 'func': func} + + def addConst(self,name,value): + self.constants[name] = value \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/__init__.py b/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/__init__.py new file mode 100644 index 00000000..9fcda82e --- /dev/null +++ b/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/__init__.py @@ -0,0 +1,2 @@ +from . import equation_base as equation_base +from .ExpressionCore import ExpressionValue, ExpressionFunction, ExpressionVariable, Core \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/equation_base.py b/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/equation_base.py new file mode 100644 index 00000000..5710c965 --- /dev/null +++ b/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/equation_base.py @@ -0,0 +1,106 @@ +try: + import numpy as np + has_numpy = True +except ImportError: + import math + has_numpy = False +try: + import scipy.constants + has_scipy = True +except ImportError: + has_scipy = False +import operator as op +from .similar import sim, nsim, gsim, lsim + +def equation_extend(core): + def product(*args): + if len(args) == 1 and has_numpy: + return np.prod(args[0]) + else: + return reduce(op.mul,args,1) + + def sumargs(*args): + if len(args) == 1: + return sum(args[0]) + else: + return sum(args) + + core.addOp('+',"({0:s} + {1:s})","\\left({0:s} + {1:s}\\right)",False,3,op.add) + core.addOp('-',"({0:s} - {1:s})","\\left({0:s} - {1:s}\\right)",False,3,op.sub) + core.addOp('*',"({0:s} * {1:s})","\\left({0:s} \\times {1:s}\\right)",False,2,op.mul) + core.addOp('/',"({0:s} / {1:s})","\\frac{{{0:s}}}{{{1:s}}}",False,2,op.truediv) + core.addOp('%',"({0:s} % {1:s})","\\left({0:s} \\bmod {1:s}\\right)",False,2,op.mod) + core.addOp('^',"({0:s} ^ {1:s})","{0:s}^{{{1:s}}}",False,1,op.pow) + core.addOp('**',"({0:s} ^ {1:s})","{0:s}^{{{1:s}}}",False,1,op.pow) + core.addOp('&',"({0:s} & {1:s})","\\left({0:s} \\land {1:s}\\right)",False,4,op.and_) + core.addOp('|',"({0:s} | {1:s})","\\left({0:s} \\lor {1:s}\\right)",False,4,op.or_) + core.addOp('',"({0:s} {1:s})","\\left({0:s} \\oplus {1:s}\\right)",False,4,op.xor) + core.addOp('&|',"({0:s} {1:s})","\\left({0:s} \\oplus {1:s}\\right)",False,4,op.xor) + core.addOp('|&',"({0:s} {1:s})","\\left({0:s} \\oplus {1:s}\\right)",False,4,op.xor) + core.addOp('==',"({0:s} == {1:s})","\\left({0:s} = {1:s}\\right)",False,5,op.eq) + core.addOp('=',"({0:s} == {1:s})","\\left({0:s} = {1:s}\\right)",False,5,op.eq) + core.addOp('~',"({0:s} ~ {1:s})","\\left({0:s} \\approx {1:s}\\right)",False,5,sim) + core.addOp('!~',"({0:s} !~ {1:s})","\\left({0:s} \\not\\approx {1:s}\\right)",False,5,nsim) + core.addOp('!=',"({0:s} != {1:s})","\\left({0:s} \\neg {1:s}\\right)",False,5,op.ne) + core.addOp('<>',"({0:s} != {1:s})","\\left({0:s} \\neg {1:s}\\right)",False,5,op.ne) + core.addOp('><',"({0:s} != {1:s})","\\left({0:s} \\neg {1:s}\\right)",False,5,op.ne) + core.addOp('<',"({0:s} < {1:s})","\\left({0:s} < {1:s}\\right)",False,5,op.lt) + core.addOp('>',"({0:s} > {1:s})","\\left({0:s} > {1:s}\\right)",False,5,op.gt) + core.addOp('<=',"({0:s} <= {1:s})","\\left({0:s} \\leq {1:s}\\right)",False,5,op.le) + core.addOp('>=',"({0:s} >= {1:s})","\\left({0:s} \\geq {1:s}\\right)",False,5,op.ge) + core.addOp('=<',"({0:s} <= {1:s})","\\left({0:s} \\leq {1:s}\\right)",False,5,op.le) + core.addOp('=>',"({0:s} >= {1:s})","\\left({0:s} \\geq {1:s}\\right)",False,5,op.ge) + core.addOp('<~',"({0:s} <~ {1:s})","\\left({0:s} \lessapprox {1:s}\\right)",False,5,lsim) + core.addOp('>~',"({0:s} >~ {1:s})","\\left({0:s} \\gtrapprox {1:s}\\right)",False,5,gsim) + core.addOp('~<',"({0:s} <~ {1:s})","\\left({0:s} \lessapprox {1:s}\\right)",False,5,lsim) + core.addOp('~>',"({0:s} >~ {1:s})","\\left({0:s} \\gtrapprox {1:s}\\right)",False,5,gsim) + core.addUnaryOp('!',"(!{0:s})","\\neg{0:s}",op.not_) + core.addUnaryOp('-',"-{0:s}","-{0:s}",op.neg) + core.addFn('abs',"abs({0:s})","\\left|{0:s}\\right|",1,op.abs) + core.addFn('sum',"sum({0:s})","\\sum\\left({0:s}\\right)",'+',sumargs) + core.addFn('prod',"prod({0:s})","\\prod\\left({0:s}\\right)",'+',product) + if has_numpy: + core.addFn('floor',"floor({0:s})","\\lfloor {0:s} \\rfloor",1,np.floor) + core.addFn('ceil',"ceil({0:s})","\\lceil {0:s} \\rceil",1,np.ceil) + core.addFn('round',"round({0:s})","\\lfloor {0:s} \\rceil",1,np.round) + core.addFn('sin',"sin({0:s})","\\sin\\left({0:s}\\right)",1,np.sin) + core.addFn('cos',"cos({0:s})","\\cos\\left({0:s}\\right)",1,np.cos) + core.addFn('tan',"tan({0:s})","\\tan\\left({0:s}\\right)",1,np.tan) + core.addFn('re',"re({0:s})","\\Re\\left({0:s}\\right)",1,np.real) + core.addFn('im',"re({0:s})","\\Im\\left({0:s}\\right)",1,np.imag) + core.addFn('sqrt',"sqrt({0:s})","\\sqrt{{{0:s}}}",1,np.sqrt) + core.addConst("pi",np.pi) + core.addConst("e",np.e) + core.addConst("Inf",np.Inf) + core.addConst("NaN",np.NaN) + else: + core.addFn('floor',"floor({0:s})","\\lfloor {0:s} \\rfloor",1,math.floor) + core.addFn('ceil',"ceil({0:s})","\\lceil {0:s} \\rceil",1,math.ceil) + core.addFn('round',"round({0:s})","\\lfloor {0:s} \\rceil",1,round) + core.addFn('sin',"sin({0:s})","\\sin\\left({0:s}\\right)",1,math.sin) + core.addFn('cos',"cos({0:s})","\\cos\\left({0:s}\\right)",1,math.cos) + core.addFn('tan',"tan({0:s})","\\tan\\left({0:s}\\right)",1,math.tan) + core.addFn('re',"re({0:s})","\\Re\\left({0:s}\\right)",1,complex.real) + core.addFn('im',"re({0:s})","\\Im\\left({0:s}\\right)",1,complex.imag) + core.addFn('sqrt',"sqrt({0:s})","\\sqrt{{{0:s}}}",1,math.sqrt) + core.addConst("pi",math.pi) + core.addConst("e",math.e) + core.addConst("Inf",float("Inf")) + core.addConst("NaN",float("NaN")) + if has_scipy: + core.addConst("h",scipy.constants.h) + core.addConst("hbar",scipy.constants.hbar) + core.addConst("m_e",scipy.constants.m_e) + core.addConst("m_p",scipy.constants.m_p) + core.addConst("m_n",scipy.constants.m_n) + core.addConst("c",scipy.constants.c) + core.addConst("N_A",scipy.constants.N_A) + core.addConst("mu_0",scipy.constants.mu_0) + core.addConst("eps_0",scipy.constants.epsilon_0) + core.addConst("k",scipy.constants.k) + core.addConst("G",scipy.constants.G) + core.addConst("g",scipy.constants.g) + core.addConst("q",scipy.constants.e) + core.addConst("R",scipy.constants.R) + core.addConst("sigma",scipy.constants.e) + core.addConst("Rb",scipy.constants.Rydberg) \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/similar.py b/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/similar.py new file mode 100644 index 00000000..faf2979e --- /dev/null +++ b/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/similar.py @@ -0,0 +1,49 @@ +_tol = 1e-5 + +def sim(a,b): + if (a==b): + return True + elif a == 0 or b == 0: + return False + if (a_tol + else: + return (1-b/a)>_tol + +def gsim(a,b): + if a >= b: + return True + return (1-a/b)<=_tol + +def lsim(a,b): + if a <= b: + return True + return (1-b/a)<=_tol + +def set_tol(value=1e-5): + r"""Set Error Tolerance + + Set the tolerance for detriming if two numbers are simliar, i.e + :math:`\left|\frac{a}{b}\right| = 1 \pm tolerance` + + Parameters + ---------- + value: float + The Value to set the tolerance to show be very small as it respresents the + percentage of acceptable error in detriming if two values are the same. + """ + global _tol + if isinstance(value,float): + _tol = value + else: + raise TypeError(type(value)) \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/parser/__init__.py b/analysis-master/tra_analysis/equation/parser/__init__.py index 17b284ed..7c0a4447 100644 --- a/analysis-master/tra_analysis/equation/parser/__init__.py +++ b/analysis-master/tra_analysis/equation/parser/__init__.py @@ -2,14 +2,13 @@ # Written by Arthur Lu # Notes: # this should be imported as a python module using 'from tra_analysis.Equation import parser' -# adapted from https://github.com/pyparsing/pyparsing/blob/master/examples/fourFn.py # setup: __version__ = "0.0.4-alpha" __changelog__ = """changelog: 0.0.4-alpha: - - moved individual parsers to their own filespar + - moved individual parsers to their own files 0.0.3-alpha: - readded old regex based parser as RegexInplaceParser 0.0.2-alpha: @@ -25,8 +24,11 @@ __author__ = ( __all__ = { "BNF", - "RegexInplaceParser" + "RegexInplaceParser", + "HybridExpressionParser" } from .BNF import BNF as BNF -from .RegexInplaceParser import RegexInplaceParser as RegexInplaceParser \ No newline at end of file +from .RegexInplaceParser import RegexInplaceParser as RegexInplaceParser +from .Hybrid import HybridExpressionParser +from .Hybrid_Utils import equation_base, Core \ No newline at end of file diff --git a/analysis-master/tra_analysis/equation/parser/py2.py b/analysis-master/tra_analysis/equation/parser/py2.py index e9afa9a5..862f0e98 100644 --- a/analysis-master/tra_analysis/equation/parser/py2.py +++ b/analysis-master/tra_analysis/equation/parser/py2.py @@ -10,7 +10,7 @@ __version__ = "1.0.0" __changelog__ = """changelog: 1.0.0: - - added cmp function + - added cmp function """ __author__ = ( From 8f54b8e3fc1a77f022b7891aeb2806943e48a310 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Wed, 9 Dec 2020 04:02:28 +0000 Subject: [PATCH 21/24] added Equation to tra_analysis imports --- analysis-master/tra_analysis/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/analysis-master/tra_analysis/__init__.py b/analysis-master/tra_analysis/__init__.py index 6ea9c9b7..af00cc72 100644 --- a/analysis-master/tra_analysis/__init__.py +++ b/analysis-master/tra_analysis/__init__.py @@ -29,6 +29,7 @@ from . import Analysis from .Array import Array from .ClassificationMetric import ClassificationMetric from . import CorrelationTest +from . import Equation from . import Fit from . import KNN from . import NaiveBayes From f72d8457a74bfce7167b2bae679f7e512f29a92f Mon Sep 17 00:00:00 2001 From: zpan1 <72054510+zpan1@users.noreply.github.com> Date: Tue, 26 Jan 2021 21:46:29 -0600 Subject: [PATCH 22/24] tests: New unit tests for submoduling (#66) * feat: created kivy gui boilerplate * migrated docker base image to debian Signed-off-by: ltcptgeneral * migrated to ubuntu Signed-off-by: ltcptgeneral * fixed issues Signed-off-by: ltcptgeneral * fix: docker build? * fix: use ubuntu bionic * fix: get kivy installed * @ltcptgeneral can't spell * optim dockerfile for not installing unused packages * install basic stuff while building the container * use prebuilt image for development * install pylint on base image * rename and use new kivy * tests: added tests for Array and CorrelationTest Both are not working due to errors * fix: Array no longer has *args and CorrelationTest functions no longer have self in the arguments * use new thing * use 20.04 base * symlink pip3 to pip * use pip instead of pip3 * tra_analysis v 2.1.0-alpha.2 SVM v 1.0.1 added unvalidated SVM unit tests Signed-off-by: ltcptgeneral * fixed version number Signed-off-by: ltcptgeneral * tests: added tests for ClassificationMetric * partially fixed and commented out svm unit tests * fixed some SVM unit tests * added installing pytest to devcontainer.json * fix: small fixes to KNN Namely, removing self from parameters and passing correct arguments to KNeighborsClassifier constructor * fix, test: Added tests for KNN and NaiveBayes. Also made some small fixes in KNN, NaiveBayes, and RegressionMetric * test: finished unit tests except for StatisticalTest Also made various small fixes and style changes * StatisticalTest v 1.0.1 * fixed RegressionMetric unit test temporarily disabled CorrelationTest unit tests * tra_analysis v 2.1.0-alpha.3 * readded __all__ * fix: floating point issues in unit tests for CorrelationTest Co-authored-by: AGawde05 Co-authored-by: ltcptgeneral Co-authored-by: Dev Singh Co-authored-by: jzpan1 --- .devcontainer/Dockerfile | 9 +- .devcontainer/dev-dockerfile | 2 + .devcontainer/devcontainer.json | 4 +- analysis-master/test_analysis.py | 103 +++++++++++++++++- analysis-master/tra_analysis/Analysis.py | 2 +- analysis-master/tra_analysis/Array.py | 56 ++++------ .../tra_analysis/CorrelationTest.py | 14 +-- analysis-master/tra_analysis/KNN.py | 11 +- analysis-master/tra_analysis/NaiveBayes.py | 6 +- analysis-master/tra_analysis/RandomForest.py | 2 +- .../tra_analysis/RegressionMetric.py | 4 +- analysis-master/tra_analysis/SVM.py | 61 ++++++----- .../tra_analysis/StatisticalTest.py | 37 ++++++- analysis-master/tra_analysis/__init__.py | 20 ++-- data-analysis/design.kv | 46 ++++++++ data-analysis/main.py | 41 +++++++ 16 files changed, 322 insertions(+), 96 deletions(-) create mode 100644 .devcontainer/dev-dockerfile create mode 100644 data-analysis/design.kv create mode 100644 data-analysis/main.py diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 4cb82cc3..160a9b91 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,2 +1,7 @@ -FROM python:3.8 -WORKDIR ~/ \ No newline at end of file +FROM ubuntu:20.04 +WORKDIR / +RUN apt-get -y update +RUN DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends tzdata +RUN apt-get install -y python3 python3-dev git python3-pip python3-kivy python-is-python3 libgl1-mesa-dev build-essential +RUN ln -s $(which pip3) /usr/bin/pip +RUN pip install pymongo pandas numpy scipy scikit-learn matplotlib pylint kivy \ No newline at end of file diff --git a/.devcontainer/dev-dockerfile b/.devcontainer/dev-dockerfile new file mode 100644 index 00000000..74659d49 --- /dev/null +++ b/.devcontainer/dev-dockerfile @@ -0,0 +1,2 @@ +FROM titanscout2022/tra-analysis-base:latest +WORKDIR / \ No newline at end of file diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 3020c639..e670608d 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,7 +1,7 @@ { "name": "TRA Analysis Development Environment", "build": { - "dockerfile": "Dockerfile", + "dockerfile": "dev-dockerfile", }, "settings": { "terminal.integrated.shell.linux": "/bin/bash", @@ -24,5 +24,5 @@ "ms-python.python", "waderyan.gitblame" ], - "postCreateCommand": "apt install vim -y ; pip install -r data-analysis/requirements.txt ; pip install -r analysis-master/requirements.txt ; pip install --no-cache-dir pylint ; pip install --no-cache-dir tra-analysis" + "postCreateCommand": "/usr/bin/pip3 install -r /workspaces/red-alliance-analysis/data-analysis/requirements.txt && /usr/bin/pip3 install -r /workspaces/red-alliance-analysis/analysis-master/requirements.txt && /usr/bin/pip3 install --no-cache-dir pylint && pip3 install pytest" } \ No newline at end of file diff --git a/analysis-master/test_analysis.py b/analysis-master/test_analysis.py index 0cb41003..5676ca08 100644 --- a/analysis-master/test_analysis.py +++ b/analysis-master/test_analysis.py @@ -1,3 +1,7 @@ +import numpy as np +import sklearn +from sklearn import metrics + from tra_analysis import Analysis as an from tra_analysis import Array from tra_analysis import ClassificationMetric @@ -12,13 +16,27 @@ from tra_analysis import StatisticalTest from tra_analysis import SVM def test_(): + test_data_linear = [1, 3, 6, 7, 9] + test_data_linear2 = [2, 2, 5, 7, 13] + test_data_array = Array(test_data_linear) + x_data_circular = [] y_data_circular = [] + y_data_ccu = [1, 3, 7, 14, 21] y_data_ccd = [1, 5, 7, 8.5, 8.66] + test_data_scrambled = [-32, 34, 19, 72, -65, -11, -43, 6, 85, -17, -98, -26, 12, 20, 9, -92, -40, 98, -78, 17, -20, 49, 93, -27, -24, -66, 40, 84, 1, -64, -68, -25, -42, -46, -76, 43, -3, 30, -14, -34, -55, -13, 41, -30, 0, -61, 48, 23, 60, 87, 80, 77, 53, 73, 79, 24, -52, 82, 8, -44, 65, 47, -77, 94, 7, 37, -79, 36, -94, 91, 59, 10, 97, -38, -67, 83, 54, 31, -95, -63, 16, -45, 21, -12, 66, -48, -18, -96, -90, -21, -83, -74, 39, 64, 69, -97, 13, 55, 27, -39] test_data_sorted = [-98, -97, -96, -95, -94, -92, -90, -83, -79, -78, -77, -76, -74, -68, -67, -66, -65, -64, -63, -61, -55, -52, -48, -46, -45, -44, -43, -42, -40, -39, -38, -34, -32, -30, -27, -26, -25, -24, -21, -20, -18, -17, -14, -13, -12, -11, -3, 0, 1, 6, 7, 8, 9, 10, 12, 13, 16, 17, 19, 20, 21, 23, 24, 27, 30, 31, 34, 36, 37, 39, 40, 41, 43, 47, 48, 49, 53, 54, 55, 59, 60, 64, 65, 66, 69, 72, 73, 77, 79, 80, 82, 83, 84, 85, 87, 91, 93, 94, 97, 98] + + test_data_2D_pairs = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) + test_data_2D_positive = np.array([[23, 51], [21, 32], [15, 25], [17, 31]]) + test_output = np.array([1, 3, 4, 5]) + test_labels_2D_pairs = np.array([1, 1, 2, 2]) + validation_data_2D_pairs = np.array([[-0.8, -1], [0.8, 1.2]]) + validation_labels_2D_pairs = np.array([1, 2]) + assert an.basic_stats(test_data_linear) == {"mean": 5.2, "median": 6.0, "standard-deviation": 2.85657137141714, "variance": 8.16, "minimum": 1.0, "maximum": 9.0} assert an.z_score(3.2, 6, 1.5) == -1.8666666666666665 assert an.z_normalize([test_data_linear], 1).tolist() == [[0.07537783614444091, 0.22613350843332272, 0.45226701686664544, 0.5276448530110863, 0.6784005252999682]] @@ -30,6 +48,58 @@ def test_(): assert an.Metric().elo(1500, 1500, [1, 0], 400, 24) == 1512.0 assert an.Metric().glicko2(1500, 250, 0.06, [1500, 1400], [250, 240], [1, 0]) == (1478.864307445517, 195.99122679202452, 0.05999602937563585) #assert an.Metric().trueskill([[(25, 8.33), (24, 8.25), (32, 7.5)], [(25, 8.33), (25, 8.33), (21, 6.5)]], [1, 0]) == [(metrics.trueskill.Rating(mu=21.346, sigma=7.875), metrics.trueskill.Rating(mu=20.415, sigma=7.808), metrics.trueskill.Rating(mu=29.037, sigma=7.170)), (metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=28.654, sigma=7.875), metrics.trueskill.Rating(mu=23.225, sigma=6.287))] + + assert test_data_array.elementwise_mean() == 5.2 + assert test_data_array.elementwise_median() == 6.0 + assert test_data_array.elementwise_stdev() == 2.85657137141714 + assert test_data_array.elementwise_variance() == 8.16 + assert test_data_array.elementwise_npmin() == 1 + assert test_data_array.elementwise_npmax() == 9 + assert test_data_array.elementwise_stats() == (5.2, 6.0, 2.85657137141714, 8.16, 1, 9) + + classif_metric = ClassificationMetric(test_data_linear2, test_data_linear) + assert classif_metric[0].all() == metrics.confusion_matrix(test_data_linear, test_data_linear2).all() + assert classif_metric[1] == metrics.classification_report(test_data_linear, test_data_linear2) + + assert all(np.isclose(list(CorrelationTest.anova_oneway(test_data_linear, test_data_linear2).values()), [0.05825242718446602, 0.8153507906592907], rtol=1e-10)) + assert all(np.isclose(list(CorrelationTest.pearson(test_data_linear, test_data_linear2).values()), [0.9153061540753287, 0.02920895440940868], rtol=1e-10)) + assert all(np.isclose(list(CorrelationTest.spearman(test_data_linear, test_data_linear2).values()), [0.9746794344808964, 0.004818230468198537], rtol=1e-10)) + assert all(np.isclose(list(CorrelationTest.point_biserial(test_data_linear, test_data_linear2).values()), [0.9153061540753287, 0.02920895440940868], rtol=1e-10)) + assert all(np.isclose(list(CorrelationTest.kendall(test_data_linear, test_data_linear2).values()), [0.9486832980505137, 0.022977401503206086], rtol=1e-10)) + assert all(np.isclose(list(CorrelationTest.kendall_weighted(test_data_linear, test_data_linear2).values()), [0.9750538072369643, np.nan], rtol=1e-10, equal_nan=True)) + + assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0) + + model, metric = KNN.knn_classifier(test_data_2D_pairs, test_labels_2D_pairs, 2) + assert isinstance(model, sklearn.neighbors.KNeighborsClassifier) + assert np.array([[0,0], [2,0]]).all() == metric[0].all() + assert ' precision recall f1-score support\n\n 1 0.00 0.00 0.00 0.0\n 2 0.00 0.00 0.00 2.0\n\n accuracy 0.00 2.0\n macro avg 0.00 0.00 0.00 2.0\nweighted avg 0.00 0.00 0.00 2.0\n' == metric[1] + model, metric = KNN.knn_regressor(test_data_2D_pairs, test_output, 2) + assert isinstance(model, sklearn.neighbors.KNeighborsRegressor) + assert (-25.0, 6.5, 2.5495097567963922) == metric + + model, metric = NaiveBayes.gaussian(test_data_2D_pairs, test_labels_2D_pairs) + assert isinstance(model, sklearn.naive_bayes.GaussianNB) + assert metric[0].all() == np.array([[0, 0], [2, 0]]).all() + model, metric = NaiveBayes.multinomial(test_data_2D_positive, test_labels_2D_pairs) + assert isinstance(model, sklearn.naive_bayes.MultinomialNB) + assert metric[0].all() == np.array([[0, 0], [2, 0]]).all() + model, metric = NaiveBayes.bernoulli(test_data_2D_pairs, test_labels_2D_pairs) + assert isinstance(model, sklearn.naive_bayes.BernoulliNB) + assert metric[0].all() == np.array([[0, 0], [2, 0]]).all() + model, metric = NaiveBayes.complement(test_data_2D_positive, test_labels_2D_pairs) + assert isinstance(model, sklearn.naive_bayes.ComplementNB) + assert metric[0].all() == np.array([[0, 0], [2, 0]]).all() + + model, metric = RandomForest.random_forest_classifier(test_data_2D_pairs, test_labels_2D_pairs, 0.3, 2) + assert isinstance(model, sklearn.ensemble.RandomForestClassifier) + assert metric[0].all() == np.array([[0, 0], [2, 0]]).all() + model, metric = RandomForest.random_forest_regressor(test_data_2D_pairs, test_labels_2D_pairs, 0.3, 2) + assert isinstance(model, sklearn.ensemble.RandomForestRegressor) + assert metric == (0.0, 1.0, 1.0) + + assert RegressionMetric(test_data_linear, test_data_linear2)== (0.7705314009661837, 3.8, 1.9493588689617927) + assert all(a == b for a, b in zip(Sort.quicksort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(Sort.mergesort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(Sort.heapsort(test_data_scrambled), test_data_sorted)) @@ -41,4 +111,35 @@ def test_(): assert all(a == b for a, b in zip(Sort.bubblesort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(Sort.cyclesort(test_data_scrambled), test_data_sorted)) assert all(a == b for a, b in zip(Sort.cocktailsort(test_data_scrambled), test_data_sorted)) - assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0) \ No newline at end of file + + assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0) + + svm(test_data_2D_pairs, test_labels_2D_pairs, validation_data_2D_pairs, validation_labels_2D_pairs) + +def svm(data, labels, test_data, test_labels): + + lin_kernel = SVM.PrebuiltKernel.Linear() + #ply_kernel = SVM.PrebuiltKernel.Polynomial(3, 0) + rbf_kernel = SVM.PrebuiltKernel.RBF('scale') + sig_kernel = SVM.PrebuiltKernel.Sigmoid(0) + + lin_kernel = SVM.fit(lin_kernel, data, labels) + #ply_kernel = SVM.fit(ply_kernel, data, labels) + rbf_kernel = SVM.fit(rbf_kernel, data, labels) + sig_kernel = SVM.fit(sig_kernel, data, labels) + + for i in range(len(test_data)): + + assert lin_kernel.predict([test_data[i]]).tolist() == [test_labels[i]] + + #for i in range(len(test_data)): + + # assert ply_kernel.predict([test_data[i]]).tolist() == [test_labels[i]] + + for i in range(len(test_data)): + + assert rbf_kernel.predict([test_data[i]]).tolist() == [test_labels[i]] + + for i in range(len(test_data)): + + assert sig_kernel.predict([test_data[i]]).tolist() == [test_labels[i]] diff --git a/analysis-master/tra_analysis/Analysis.py b/analysis-master/tra_analysis/Analysis.py index cfe66455..287d6fbb 100644 --- a/analysis-master/tra_analysis/Analysis.py +++ b/analysis-master/tra_analysis/Analysis.py @@ -397,7 +397,7 @@ from .RandomForest_obj import RandomForest from .RegressionMetric import RegressionMetric from .Sort_obj import Sort from .StatisticalTest_obj import StatisticalTest -from .SVM import SVM +from . import SVM class error(ValueError): pass diff --git a/analysis-master/tra_analysis/Array.py b/analysis-master/tra_analysis/Array.py index ab02c9a0..bd0b626a 100644 --- a/analysis-master/tra_analysis/Array.py +++ b/analysis-master/tra_analysis/Array.py @@ -27,55 +27,37 @@ class Array(): # tests on nd arrays independent of basic_stats return str(self.array) - def elementwise_mean(self, *args, axis = 0): # expects arrays that are size normalized - if len(*args) == 0: - return np.mean(self.array, axis = axis) - else: - return np.mean([*args], axis = axis) + def elementwise_mean(self, axis = 0): # expects arrays that are size normalized - def elementwise_median(self, *args, axis = 0): + return np.mean(self.array, axis = axis) - if len(*args) == 0: - return np.median(self.array, axis = axis) - else: - return np.median([*args], axis = axis) + def elementwise_median(self, axis = 0): - def elementwise_stdev(self, *args, axis = 0): + return np.median(self.array, axis = axis) - if len(*args) == 0: - return np.std(self.array, axis = axis) - else: - return np.std([*args], axis = axis) + def elementwise_stdev(self, axis = 0): - def elementwise_variance(self, *args, axis = 0): + return np.std(self.array, axis = axis) - if len(*args) == 0: - return np.var(self.array, axis = axis) - else: - return np.var([*args], axis = axis) + def elementwise_variance(self, axis = 0): - def elementwise_npmin(self, *args, axis = 0): + return np.var(self.array, axis = axis) - if len(*args) == 0: - return np.amin(self.array, axis = axis) - else: - return np.amin([*args], axis = axis) + def elementwise_npmin(self, axis = 0): + return np.amin(self.array, axis = axis) - def elementwise_npmax(self, *args, axis = 0): - if len(*args) == 0: - return np.amax(self.array, axis = axis) - else: - return np.amax([*args], axis = axis) + def elementwise_npmax(self, axis = 0): + return np.amax(self.array, axis = axis) - def elementwise_stats(self, *args, axis = 0): + def elementwise_stats(self, axis = 0): - _mean = self.elementwise_mean(*args, axis = axis) - _median = self.elementwise_median(*args, axis = axis) - _stdev = self.elementwise_stdev(*args, axis = axis) - _variance = self.elementwise_variance(*args, axis = axis) - _min = self.elementwise_npmin(*args, axis = axis) - _max = self.elementwise_npmax(*args, axis = axis) + _mean = self.elementwise_mean(axis = axis) + _median = self.elementwise_median(axis = axis) + _stdev = self.elementwise_stdev(axis = axis) + _variance = self.elementwise_variance(axis = axis) + _min = self.elementwise_npmin(axis = axis) + _max = self.elementwise_npmax(axis = axis) return _mean, _median, _stdev, _variance, _min, _max diff --git a/analysis-master/tra_analysis/CorrelationTest.py b/analysis-master/tra_analysis/CorrelationTest.py index 1fd841ef..3de6ebbb 100644 --- a/analysis-master/tra_analysis/CorrelationTest.py +++ b/analysis-master/tra_analysis/CorrelationTest.py @@ -22,37 +22,37 @@ __all__ = [ import scipy from scipy import stats -def anova_oneway(self, *args): #expects arrays of samples +def anova_oneway(*args): #expects arrays of samples results = scipy.stats.f_oneway(*args) return {"f-value": results[0], "p-value": results[1]} -def pearson(self, x, y): +def pearson(x, y): results = scipy.stats.pearsonr(x, y) return {"r-value": results[0], "p-value": results[1]} -def spearman(self, a, b = None, axis = 0, nan_policy = 'propagate'): +def spearman(a, b = None, axis = 0, nan_policy = 'propagate'): results = scipy.stats.spearmanr(a, b = b, axis = axis, nan_policy = nan_policy) return {"r-value": results[0], "p-value": results[1]} -def point_biserial(self, x,y): +def point_biserial(x, y): results = scipy.stats.pointbiserialr(x, y) return {"r-value": results[0], "p-value": results[1]} -def kendall(self, x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'): +def kendall(x, y, initial_lexsort = None, nan_policy = 'propagate', method = 'auto'): results = scipy.stats.kendalltau(x, y, initial_lexsort = initial_lexsort, nan_policy = nan_policy, method = method) return {"tau": results[0], "p-value": results[1]} -def kendall_weighted(self, x, y, rank = True, weigher = None, additive = True): +def kendall_weighted(x, y, rank = True, weigher = None, additive = True): results = scipy.stats.weightedtau(x, y, rank = rank, weigher = weigher, additive = additive) return {"tau": results[0], "p-value": results[1]} -def mgc(self, x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None): +def mgc(x, y, compute_distance = None, reps = 1000, workers = 1, is_twosamp = False, random_state = None): results = scipy.stats.multiscale_graphcorr(x, y, compute_distance = compute_distance, reps = reps, workers = workers, is_twosamp = is_twosamp, random_state = random_state) return {"k-value": results[0], "p-value": results[1], "data": results[2]} # unsure if MGC test returns a k value \ No newline at end of file diff --git a/analysis-master/tra_analysis/KNN.py b/analysis-master/tra_analysis/KNN.py index ed9ca9df..d594c72f 100644 --- a/analysis-master/tra_analysis/KNN.py +++ b/analysis-master/tra_analysis/KNN.py @@ -14,29 +14,32 @@ __changelog__ = """changelog: __author__ = ( "Arthur Lu ", + "James Pan " ) __all__ = [ + 'knn_classifier', + 'knn_regressor' ] import sklearn from sklearn import model_selection, neighbors from . import ClassificationMetric, RegressionMetric -def knn_classifier(self, data, labels, n_neighbors, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling +def knn_classifier(data, labels, n_neighbors = 5, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) - model = sklearn.neighbors.KNeighborsClassifier() + model = sklearn.neighbors.KNeighborsClassifier(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs) model.fit(data_train, labels_train) predictions = model.predict(data_test) return model, ClassificationMetric(predictions, labels_test) -def knn_regressor(self, data, outputs, n_neighbors, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None): +def knn_regressor(data, outputs, n_neighbors = 5, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None): data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1) model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs) model.fit(data_train, outputs_train) predictions = model.predict(data_test) - return model, RegressionMetric(predictions, outputs_test) \ No newline at end of file + return model, RegressionMetric.RegressionMetric(predictions, outputs_test) \ No newline at end of file diff --git a/analysis-master/tra_analysis/NaiveBayes.py b/analysis-master/tra_analysis/NaiveBayes.py index c666975a..304d731c 100644 --- a/analysis-master/tra_analysis/NaiveBayes.py +++ b/analysis-master/tra_analysis/NaiveBayes.py @@ -16,13 +16,17 @@ __author__ = ( ) __all__ = [ + 'gaussian', + 'multinomial' + 'bernoulli', + 'complement' ] import sklearn from sklearn import model_selection, naive_bayes from . import ClassificationMetric, RegressionMetric -def guassian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09): +def gaussian(data, labels, test_size = 0.3, priors = None, var_smoothing = 1e-09): data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1) model = sklearn.naive_bayes.GaussianNB(priors = priors, var_smoothing = var_smoothing) diff --git a/analysis-master/tra_analysis/RandomForest.py b/analysis-master/tra_analysis/RandomForest.py index b39b56f7..6349c873 100644 --- a/analysis-master/tra_analysis/RandomForest.py +++ b/analysis-master/tra_analysis/RandomForest.py @@ -39,4 +39,4 @@ def random_forest_regressor(data, outputs, test_size, n_estimators, criterion="m kernel.fit(data_train, outputs_train) predictions = kernel.predict(data_test) - return kernel, RegressionMetric(predictions, outputs_test) \ No newline at end of file + return kernel, RegressionMetric.RegressionMetric(predictions, outputs_test) \ No newline at end of file diff --git a/analysis-master/tra_analysis/RegressionMetric.py b/analysis-master/tra_analysis/RegressionMetric.py index 37904a04..0b985493 100644 --- a/analysis-master/tra_analysis/RegressionMetric.py +++ b/analysis-master/tra_analysis/RegressionMetric.py @@ -16,8 +16,10 @@ __author__ = ( ) __all__ = [ + 'RegressionMetric' ] +import numpy as np import sklearn from sklearn import metrics @@ -37,4 +39,4 @@ class RegressionMetric(): def rms(self, predictions, targets): - return math.sqrt(sklearn.metrics.mean_squared_error(targets, predictions)) \ No newline at end of file + return np.sqrt(sklearn.metrics.mean_squared_error(targets, predictions)) \ No newline at end of file diff --git a/analysis-master/tra_analysis/SVM.py b/analysis-master/tra_analysis/SVM.py index ef568c5f..d1e2022b 100644 --- a/analysis-master/tra_analysis/SVM.py +++ b/analysis-master/tra_analysis/SVM.py @@ -4,9 +4,12 @@ # this should be imported as a python module using 'from tra_analysis import SVM' # setup: -__version__ = "1.0.0" +__version__ = "1.0.1" __changelog__ = """changelog: + 1.0.1: + - removed unessasary self calls + - removed classness 1.0.0: - ported analysis.SVM() here """ @@ -22,58 +25,56 @@ import sklearn from sklearn import svm from . import ClassificationMetric, RegressionMetric -class SVM: +class CustomKernel: - class CustomKernel: + def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state): - def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state): + return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) - return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) +class StandardKernel: - class StandardKernel: + def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None): - def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None): + return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) - return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state) +class PrebuiltKernel: - class PrebuiltKernel: + class Linear: - class Linear: + def __new__(cls): - def __new__(cls): + return sklearn.svm.SVC(kernel = 'linear') - return sklearn.svm.SVC(kernel = 'linear') + class Polynomial: - class Polynomial: + def __new__(cls, power, r_bias): - def __new__(cls, power, r_bias): + return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias) - return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias) + class RBF: - class RBF: + def __new__(cls, gamma): - def __new__(cls, gamma): + return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma) - return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma) + class Sigmoid: - class Sigmoid: + def __new__(cls, r_bias): - def __new__(cls, r_bias): + return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias) - return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias) +def fit(kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs - def fit(self, kernel, train_data, train_outputs): # expects *2d data, 1d labels or outputs + return kernel.fit(train_data, train_outputs) - return kernel.fit(train_data, train_outputs) +def eval_classification(kernel, test_data, test_outputs): - def eval_classification(self, kernel, test_data, test_outputs): + predictions = kernel.predict(test_data) - predictions = kernel.predict(test_data) + return ClassificationMetric(predictions, test_outputs) - return ClassificationMetric(predictions, test_outputs) +def eval_regression(kernel, test_data, test_outputs): - def eval_regression(self, kernel, test_data, test_outputs): + predictions = kernel.predict(test_data) - predictions = kernel.predict(test_data) - - return RegressionMetric(predictions, test_outputs) \ No newline at end of file + return RegressionMetric(predictions, test_outputs) \ No newline at end of file diff --git a/analysis-master/tra_analysis/StatisticalTest.py b/analysis-master/tra_analysis/StatisticalTest.py index 8bc84fbe..37006566 100644 --- a/analysis-master/tra_analysis/StatisticalTest.py +++ b/analysis-master/tra_analysis/StatisticalTest.py @@ -4,9 +4,11 @@ # this should be imported as a python module using 'from tra_analysis import StatisticalTest' # setup: -__version__ = "1.0.0" +__version__ = "1.0.1" __changelog__ = """changelog: + 1.0.1: + - fixed typo in __all__ 1.0.0: - ported analysis.StatisticalTest() here - removed classness @@ -17,6 +19,39 @@ __author__ = ( ) __all__ = [ + 'ttest_onesample', + 'ttest_independent', + 'ttest_statistic', + 'ttest_related', + 'ks_fitness', + 'chisquare', + 'powerdivergence' + 'ks_twosample', + 'es_twosample', + 'mw_rank', + 'mw_tiecorrection', + 'rankdata', + 'wilcoxon_ranksum', + 'wilcoxon_signedrank', + 'kw_htest', + 'friedman_chisquare', + 'bm_wtest', + 'combine_pvalues', + 'jb_fitness', + 'ab_equality', + 'bartlett_variance', + 'levene_variance', + 'sw_normality', + 'shapiro', + 'ad_onesample', + 'ad_ksample', + 'binomial', + 'fk_variance', + 'mood_mediantest', + 'mood_equalscale', + 'skewtest', + 'kurtosistest', + 'normaltest' ] import scipy diff --git a/analysis-master/tra_analysis/__init__.py b/analysis-master/tra_analysis/__init__.py index 5342c0fd..7e99fd3f 100644 --- a/analysis-master/tra_analysis/__init__.py +++ b/analysis-master/tra_analysis/__init__.py @@ -7,20 +7,24 @@ # current benchmark of optimization: 1.33 times faster # setup: -__version__ = "2.1.0-alpha.1" +__version__ = "2.1.0-alpha.3" # changelog should be viewed using print(analysis.__changelog__) __changelog__ = """changelog: - 2.1.0-alpha.1: - - moved multiple submodules under analysis to their own modules/files - - added header, __version__, __changelog__, __author__, __all__ (unpopulated) + 2.1.0-alpha.3: + - fixed indentation in meta data + 2.1.0-alpha.2: + - updated SVM import + 2.1.0-alpha.1: + - moved multiple submodules under analysis to their own modules/files + - added header, __version__, __changelog__, __author__, __all__ (unpopulated) """ __author__ = ( "Arthur Lu ", - "Jacob Levine ", - "Dev Singh ", - "James Pan " + "Jacob Levine ", + "Dev Singh ", + "James Pan " ) __all__ = [ @@ -37,4 +41,4 @@ from . import RandomForest from .RegressionMetric import RegressionMetric from . import Sort from . import StatisticalTest -from .SVM import SVM \ No newline at end of file +from . import SVM \ No newline at end of file diff --git a/data-analysis/design.kv b/data-analysis/design.kv new file mode 100644 index 00000000..26092f24 --- /dev/null +++ b/data-analysis/design.kv @@ -0,0 +1,46 @@ +: + GridLayout: + cols: 1 + GridLayout: + cols: 1 + padding: 15, 15 + spacing: 20, 20 + Label: + text: "User Login" + font_size: "20sp" + TextInput: + id: username + hint_text: "Username" + TextInput: + id: password + password: True + hint_text: "Password" + RelativeLayout: + Button: + text: "Login" + on_press: root.login(root.ids.username.text, root.ids.password.text) + size_hint: 0.3, 0.5 + pos_hint: {"center_x": 0.5, "center_y": 0.6} + Label: + id: login_wrong + text: "" + GridLayout: + cols: 2 + size_hint: 0.2, 0.2 + padding: 10, 10 + spacing: 10, 0 + Button: + text: "Forgot Password?" + background_color: 1, 1, 1, 0 + opacity: 1 if self.state == "normal" else 0.5 + color: 0.1, 0.7, 1, 1 + Button: + text: "Sign Up" + on_press: root.sign_up() + background_color: 1, 1, 1 , 0 + opacity: 1 if self.state == "normal" else 0.5 + color: 0.1, 0.7, 1, 1 + +: + HomeScreen: + name: "home_screen" diff --git a/data-analysis/main.py b/data-analysis/main.py new file mode 100644 index 00000000..8bf4029b --- /dev/null +++ b/data-analysis/main.py @@ -0,0 +1,41 @@ +from kivy.app import App +from kivy.lang import Builder +from kivy.uix.screenmanager import ScreenManager , Screen +from kivy.animation import Animation +from hoverable import HoverBehavior +from kivy.uix.image import Image +from kivy.uix.behaviors import ButtonBehavior +import json +from datetime import datetime +import glob +from pathlib import Path +import random + +import superscript as ss + +Builder.load_file('design.kv') + +class HomeScreen(Screen): + # def sign_up(self): + # self.manager.transition.direction = "left" + # self.manager.current = "sign_up_screen" + + # def login(self, uname, pword): + # with open ("users.json") as file: + # users = json.load(file) + # if uname in users and users[uname]["password"] == pword: + # self.manager.transition.direction = "left" + # self.manager.current = "login_screen_success" + # else: + # self.ids.login_wrong.text = "Incorrect Username or Password" + + +class RootWidget(ScreenManager): + pass + +class MainApp(App): + def build(self): + return RootWidget() + +if __name__ == "__main__": + MainApp().run() \ No newline at end of file From 106d7a7f472587108ca8892d2c112b844edc6dad Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Wed, 27 Jan 2021 04:08:27 +0000 Subject: [PATCH 23/24] fixed depreciated escape sequences --- analysis-master/tra_analysis/__init__.py | 4 +- .../parser/Hybrid_Utils/ExpressionCore.py | 40 +++++++++---------- 2 files changed, 22 insertions(+), 22 deletions(-) diff --git a/analysis-master/tra_analysis/__init__.py b/analysis-master/tra_analysis/__init__.py index af00cc72..78902773 100644 --- a/analysis-master/tra_analysis/__init__.py +++ b/analysis-master/tra_analysis/__init__.py @@ -25,11 +25,11 @@ __author__ = ( __all__ = [ ] -from . import Analysis +from . import Analysis as Analysis from .Array import Array from .ClassificationMetric import ClassificationMetric from . import CorrelationTest -from . import Equation +from .equation import Expression from . import Fit from . import KNN from . import NaiveBayes diff --git a/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/ExpressionCore.py b/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/ExpressionCore.py index be4d4e0d..f45de197 100644 --- a/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/ExpressionCore.py +++ b/analysis-master/tra_analysis/equation/parser/Hybrid_Utils/ExpressionCore.py @@ -157,56 +157,56 @@ class Core(): unary_ops = {} ops = {} functions = {} - smatch = re.compile("\s*,") - vmatch = re.compile("\s*" + smatch = re.compile(r"\s*,") + vmatch = re.compile(r"\s*" "(?:" "(?P" "(?P[+-]?)" - "\s*0o" + r"\s*0o" "(?P[0-7]+)" ")|(?P" "(?P[+-]?)" - "\s*0x" + r"\s*0x" "(?P[0-9a-fA-F]+)" ")|(?P" "(?P[+-]?)" - "\s*0b" + r"\s*0b" "(?P[01]+)" ")|(?P" "(?P[+-]?)" - "\s*" - "(?P(?:\d+\.\d+|\d+\.|\.\d+|\d+))" + r"\s*" + r"(?P(?:\d+\.\d+|\d+\.|\.\d+|\d+))" "(?:" "[Ee]" - "(?P[+-]?\d+)" + r"(?P[+-]?\d+)" ")?" "(?:" - "\s*" - "(?P(?(rvalue)\+|))?" - "\s*" + r"\s*" + r"(?P(?(rvalue)\+|))?" + r"\s*" "(?P(?(rvalue)(?(sep)[+-]?|[+-])|[+-]?)?)" - "\s*" - "(?P(?:\d+\.\d+|\d+\.|\.\d+|\d+))" + r"\s*" + r"(?P(?:\d+\.\d+|\d+\.|\.\d+|\d+))" "(?:" "[Ee]" - "(?P[+-]?\d+)" + r"(?P[+-]?\d+)" ")?" "[ij]" ")?" ")" ")") - nmatch = re.compile("\s*([a-zA-Z_][a-zA-Z0-9_]*)") - gsmatch = re.compile('\s*(\()') - gematch = re.compile('\s*(\))') + nmatch = re.compile(r"\s*([a-zA-Z_][a-zA-Z0-9_]*)") + gsmatch = re.compile(r'\s*(\()') + gematch = re.compile(r'\s*(\))') def recalculateFMatch(self): fks = sorted(self.functions.keys(), key=len, reverse=True) oks = sorted(self.ops.keys(), key=len, reverse=True) uks = sorted(self.unary_ops.keys(), key=len, reverse=True) - self.fmatch = re.compile('\s*(' + '|'.join(map(re.escape,fks)) + ')') - self.omatch = re.compile('\s*(' + '|'.join(map(re.escape,oks)) + ')') - self.umatch = re.compile('\s*(' + '|'.join(map(re.escape,uks)) + ')') + self.fmatch = re.compile(r'\s*(' + '|'.join(map(re.escape,fks)) + ')') + self.omatch = re.compile(r'\s*(' + '|'.join(map(re.escape,oks)) + ')') + self.umatch = re.compile(r'\s*(' + '|'.join(map(re.escape,uks)) + ')') def addFn(self,id,str,latex,args,func): self.functions[id] = { From 8336174f0161fbf8b8f3734e5d263bac63090f43 Mon Sep 17 00:00:00 2001 From: Arthur Lu Date: Wed, 27 Jan 2021 04:21:59 +0000 Subject: [PATCH 24/24] ficed tests, indent, import in test_analysis --- analysis-master/test_analysis.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/analysis-master/test_analysis.py b/analysis-master/test_analysis.py index 8ce87a60..df8eb003 100644 --- a/analysis-master/test_analysis.py +++ b/analysis-master/test_analysis.py @@ -15,6 +15,8 @@ from tra_analysis import Sort from tra_analysis import StatisticalTest from tra_analysis import SVM +from tra_analysis.equation.parser import BNF + def test_(): test_data_linear = [1, 3, 6, 7, 9] @@ -115,7 +117,7 @@ def test_(): assert Fit.CircleFit(x=[0,0,-1,1], y=[1, -1, 0, 0]).LSC() == (0.0, 0.0, 1.0, 0.0) svm(test_data_2D_pairs, test_labels_2D_pairs, validation_data_2D_pairs, validation_labels_2D_pairs) - test_equation() + test_equation() def svm(data, labels, test_data, test_labels): @@ -145,8 +147,6 @@ def svm(data, labels, test_data, test_labels): assert sig_kernel.predict([test_data[i]]).tolist() == [test_labels[i]] - test_equation() - def test_equation(): parser = BNF()