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analysis.py v 1.1.0.000
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data analysis/analysis/__pycache__/analysis.cpython-37.pyc
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data analysis/analysis/analysis-low.py
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data analysis/analysis/analysis-low.py
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# Titan Robotics Team 2022: Data Analysis Module
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# Written by Arthur Lu & Jacob Levine
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# Notes:
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# this should be imported as a python module using 'import analysis'
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# this should be included in the local directory or environment variable
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# this module has not been optimized for multhreaded computing
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# number of easter eggs: 2
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# setup:
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__version__ = "1.0.9.000"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.0.9.000:
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- refactored
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- numpyed everything
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- removed stats in favor of numpy functions
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1.0.8.005:
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- minor fixes
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1.0.8.004:
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- removed a few unused dependencies
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1.0.8.003:
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- added p_value function
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1.0.8.002:
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- updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
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1.0.8.001:
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- refactors
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- bugfixes
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1.0.8.000:
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- depreciated histo_analysis_old
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- depreciated debug
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- altered basic_analysis to take array data instead of filepath
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- refactor
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- optimization
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1.0.7.002:
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- bug fixes
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1.0.7.001:
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- bug fixes
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1.0.7.000:
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- added tanh_regression (logistical regression)
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- bug fixes
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1.0.6.005:
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- added z_normalize function to normalize dataset
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- bug fixes
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1.0.6.004:
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- bug fixes
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1.0.6.003:
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- bug fixes
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1.0.6.002:
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- bug fixes
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1.0.6.001:
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- corrected __all__ to contain all of the functions
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1.0.6.000:
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- added calc_overfit, which calculates two measures of overfit, error and performance
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- added calculating overfit to optimize_regression
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1.0.5.000:
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- added optimize_regression function, which is a sample function to find the optimal regressions
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- optimize_regression function filters out some overfit funtions (functions with r^2 = 1)
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- planned addition: overfit detection in the optimize_regression function
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1.0.4.002:
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- added __changelog__
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- updated debug function with log and exponential regressions
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1.0.4.001:
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- added log regressions
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- added exponential regressions
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- added log_regression and exp_regression to __all__
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1.0.3.008:
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- added debug function to further consolidate functions
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1.0.3.007:
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- added builtin benchmark function
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- added builtin random (linear) data generation function
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- added device initialization (_init_device)
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1.0.3.006:
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- reorganized the imports list to be in alphabetical order
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- added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives
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1.0.3.005:
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- major bug fixes
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- updated historical analysis
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- depreciated old historical analysis
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1.0.3.004:
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- added __version__, __author__, __all__
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- added polynomial regression
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- added root mean squared function
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- added r squared function
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1.0.3.003:
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- bug fixes
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- added c_entities
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1.0.3.002:
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- bug fixes
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- added nc_entities, obstacles, objectives
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- consolidated statistics.py to analysis.py
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1.0.3.001:
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- compiled 1d, column, and row basic stats into basic stats function
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1.0.3.000:
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- added historical analysis function
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1.0.2.xxx:
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- added z score test
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1.0.1.xxx:
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- major bug fixes
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1.0.0.xxx:
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- added loading csv
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- added 1d, column, row basic stats
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"""
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>, "
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"Jacob Levine <jlevine@ttic.edu>,"
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)
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__all__ = [
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'_init_device',
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'c_entities',
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'nc_entities',
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'obstacles',
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'objectives',
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'load_csv',
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'basic_stats',
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'z_score',
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'z_normalize',
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'stdev_z_split',
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'histo_analysis',
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'poly_regression',
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'log_regression',
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'exp_regression',
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'r_squared',
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'rms',
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'calc_overfit',
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'strip_data',
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'optimize_regression',
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'select_best_regression',
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'basic_analysis',
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# all statistics functions left out due to integration in other functions
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]
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# now back to your regularly scheduled programming:
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# imports (now in alphabetical order! v 1.0.3.006):
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from bisect import bisect_left, bisect_right
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import collections
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import csv
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from decimal import Decimal
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import functools
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from fractions import Fraction
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from itertools import groupby
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import math
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import matplotlib
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import numbers
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import numpy as np
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import pandas
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import random
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import scipy
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from scipy.optimize import curve_fit
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from scipy import stats
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from sklearn import *
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# import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
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import time
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import torch
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class error(ValueError):
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pass
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def _init_device(setting, arg): # initiates computation device for ANNs
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if setting == "cuda":
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try:
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return torch.device(setting + ":" + str(arg) if torch.cuda.is_available() else "cpu")
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except:
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raise error("could not assign cuda or cpu")
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elif setting == "cpu":
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try:
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return torch.device("cpu")
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except:
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raise error("could not assign cpu")
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else:
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raise error("specified device does not exist")
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def load_csv(filepath):
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with open(filepath, newline='') as csvfile:
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file_array = np.array(list(csv.reader(csvfile)))
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csvfile.close()
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return file_array
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# data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row
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def basic_stats(data, method, arg):
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if method == 'debug':
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return "basic_stats requires 3 args: data, mode, arg; where data is data to be analyzed, mode is an int from 0 - 2 depending on type of analysis (by column or by row) and is only applicable to 2d arrays (for 1d arrays use mode 1), and arg is row/column number for mode 1 or mode 2; function returns: [mean, median, mode, stdev, variance]"
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if method == "1d" or method == 0:
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data_t = np.array(data).astype(float)
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_mean = mean(data_t)
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_median = median(data_t)
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try:
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_mode = mode(data_t)
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except:
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_mode = None
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try:
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_stdev = stdev(data_t)
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except:
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_stdev = None
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try:
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_variance = variance(data_t)
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except:
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_variance = None
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return _mean, _median, _mode, _stdev, _variance
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"""
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elif method == "column" or method == 1:
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c_data = []
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c_data_sorted = []
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for i in data:
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try:
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c_data.append(float(i[arg]))
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except:
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pass
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_mean = mean(c_data)
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_median = median(c_data)
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try:
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_mode = mode(c_data)
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except:
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_mode = None
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try:
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_stdev = stdev(c_data)
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except:
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_stdev = None
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try:
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_variance = variance(c_data)
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except:
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_variance = None
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return _mean, _median, _mode, _stdev, _variance
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elif method == "row" or method == 2:
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r_data = []
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for i in range(len(data[arg])):
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r_data.append(float(data[arg][i]))
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_mean = mean(r_data)
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_median = median(r_data)
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try:
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_mode = mode(r_data)
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except:
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_mode = None
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try:
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_stdev = stdev(r_data)
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except:
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_stdev = None
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try:
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_variance = variance(r_data)
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except:
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_variance = None
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return _mean, _median, _mode, _stdev, _variance
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else:
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raise error("method error")
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"""
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# returns z score with inputs of point, mean and standard deviation of spread
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def z_score(point, mean, stdev):
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score = (point - mean) / stdev
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return score
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# mode is either 'x' or 'y' or 'both' depending on the variable(s) to be normalized
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def z_normalize(x, y, mode):
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x_norm = np.array().astype(float)
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y_norm = np.array().astype(float)
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mean = 0
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stdev = 0
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if mode == 'x':
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_mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0)
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for i in range(0, len(x), 1):
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x_norm.append(z_score(x[i], _mean, _stdev))
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return x_norm, y
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if mode == 'y':
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_mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0)
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for i in range(0, len(y), 1):
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y_norm.append(z_score(y[i], _mean, _stdev))
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return x, y_norm
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if mode == 'both':
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_mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0)
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for i in range(0, len(x), 1):
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x_norm.append(z_score(x[i], _mean, _stdev))
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_mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0)
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for i in range(0, len(y), 1):
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y_norm.append(z_score(y[i], _mean, _stdev))
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return x_norm, y_norm
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else:
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return error('method error')
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# returns n-th percentile of spread given mean, standard deviation, lower z-score, and upper z-score
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def stdev_z_split(mean, stdev, delta, low_bound, high_bound):
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z_split = np.array().astype(float)
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i = low_bound
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while True:
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z_split.append(float((1 / (stdev * math.sqrt(2 * math.pi))) *
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math.e ** (-0.5 * (((i - mean) / stdev) ** 2))))
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i = i + delta
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if i > high_bound:
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break
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return z_split
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def histo_analysis(hist_data, delta, low_bound, high_bound):
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if hist_data == 'debug':
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return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number of standard deviations')
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derivative = []
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for i in range(0, len(hist_data), 1):
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try:
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derivative.append(float(hist_data[i - 1]) - float(hist_data[i]))
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except:
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pass
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derivative_sorted = sorted(derivative, key=int)
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mean_derivative = basic_stats(derivative_sorted, "1d", 0)[0]
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stdev_derivative = basic_stats(derivative_sorted, "1d", 0)[3]
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predictions = []
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pred_change = 0
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i = low_bound
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while True:
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if i > high_bound:
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break
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try:
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pred_change = mean_derivative + i * stdev_derivative
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except:
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pred_change = mean_derivative
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predictions.append(float(hist_data[-1:][0]) + pred_change)
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i = i + delta
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return predictions
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def poly_regression(x, y, power):
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if x == "null": # if x is 'null', then x will be filled with integer points between 1 and the size of y
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x = []
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for i in range(len(y)):
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print(i)
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x.append(i + 1)
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reg_eq = scipy.polyfit(x, y, deg=power)
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eq_str = ""
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for i in range(0, len(reg_eq), 1):
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if i < len(reg_eq) - 1:
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eq_str = eq_str + str(reg_eq[i]) + \
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"*(z**" + str(len(reg_eq) - i - 1) + ")+"
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else:
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eq_str = eq_str + str(reg_eq[i]) + \
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"*(z**" + str(len(reg_eq) - i - 1) + ")"
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vals = []
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for i in range(0, len(x), 1):
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z = x[i]
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try:
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exec("vals.append(" + eq_str + ")")
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except:
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pass
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_rms = rms(vals, y)
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r2_d2 = r_squared(vals, y)
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return [eq_str, _rms, r2_d2]
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def log_regression(x, y, base):
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x_fit = []
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for i in range(len(x)):
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try:
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# change of base for logs
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x_fit.append(np.log(x[i]) / np.log(base))
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except:
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pass
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# y = reg_eq[0] * log(x, base) + reg_eq[1]
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reg_eq = np.polyfit(x_fit, y, 1)
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q_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + \
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str(base) + "))+" + str(reg_eq[1])
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vals = []
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for i in range(len(x)):
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z = x[i]
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try:
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exec("vals.append(" + eq_str + ")")
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except:
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pass
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_rms = rms(vals, y)
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r2_d2 = r_squared(vals, y)
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return eq_str, _rms, r2_d2
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def exp_regression(x, y, base):
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y_fit = []
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for i in range(len(y)):
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try:
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# change of base for logs
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y_fit.append(np.log(y[i]) / np.log(base))
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except:
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pass
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# y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1])
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reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit))
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eq_str = "(" + str(base) + "**(" + \
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str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))"
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vals = []
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for i in range(len(x)):
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z = x[i]
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try:
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exec("vals.append(" + eq_str + ")")
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except:
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pass
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_rms = rms(vals, y)
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r2_d2 = r_squared(vals, y)
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return eq_str, _rms, r2_d2
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def tanh_regression(x, y):
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def tanh(x, a, b, c, d):
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return a * np.tanh(b * (x - c)) + d
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reg_eq = np.float64(curve_fit(tanh, np.array(x), np.array(y))[0]).tolist()
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eq_str = str(reg_eq[0]) + " * np.tanh(" + str(reg_eq[1]) + \
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||||
"*(z - " + str(reg_eq[2]) + ")) + " + str(reg_eq[3])
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||||
vals = []
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||||
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||||
for i in range(len(x)):
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||||
z = x[i]
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||||
try:
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||||
exec("vals.append(" + eq_str + ")")
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||||
except:
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||||
pass
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||||
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||||
_rms = rms(vals, y)
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||||
r2_d2 = r_squared(vals, y)
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||||
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||||
return eq_str, _rms, r2_d2
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||||
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||||
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def r_squared(predictions, targets): # assumes equal size inputs
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||||
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||||
return metrics.r2_score(np.array(targets), np.array(predictions))
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||||
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||||
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||||
def rms(predictions, targets): # assumes equal size inputs
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||||
|
||||
_sum = 0
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||||
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||||
for i in range(0, len(targets), 1):
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||||
_sum = (targets[i] - predictions[i]) ** 2
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||||
|
||||
return float(math.sqrt(_sum / len(targets)))
|
||||
|
||||
|
||||
def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
|
||||
|
||||
# performance overfit = performance(train) - performance(test) where performance is r^2
|
||||
# error overfit = error(train) - error(test) where error is rms; biased towards smaller values
|
||||
|
||||
vals = []
|
||||
|
||||
for i in range(0, len(x_test), 1):
|
||||
|
||||
z = x_test[i]
|
||||
|
||||
exec("vals.append(" + equation + ")")
|
||||
|
||||
r2_test = r_squared(vals, y_test)
|
||||
rms_test = rms(vals, y_test)
|
||||
|
||||
return r2_train - r2_test
|
||||
|
||||
|
||||
def strip_data(data, mode):
|
||||
|
||||
if mode == "adam": # x is the row number, y are the data
|
||||
pass
|
||||
|
||||
if mode == "eve": # x are the data, y is the column number
|
||||
pass
|
||||
|
||||
else:
|
||||
raise error("mode error")
|
||||
|
||||
|
||||
# _range in poly regression is the range of powers tried, and in log/exp it is the inverse of the stepsize taken from -1000 to 1000
|
||||
def optimize_regression(x, y, _range, resolution):
|
||||
# usage not: for demonstration purpose only, performance is shit
|
||||
if type(resolution) != int:
|
||||
raise error("resolution must be int")
|
||||
|
||||
x_train = x
|
||||
y_train = []
|
||||
|
||||
for i in range(len(y)):
|
||||
y_train.append(float(y[i]))
|
||||
|
||||
x_test = []
|
||||
y_test = []
|
||||
|
||||
for i in range(0, math.floor(len(x) * 0.5), 1):
|
||||
index = random.randint(0, len(x) - 1)
|
||||
|
||||
x_test.append(x[index])
|
||||
y_test.append(float(y[index]))
|
||||
|
||||
x_train.pop(index)
|
||||
y_train.pop(index)
|
||||
|
||||
#print(x_train, x_test)
|
||||
#print(y_train, y_test)
|
||||
|
||||
eqs = []
|
||||
rmss = []
|
||||
r2s = []
|
||||
|
||||
for i in range(0, _range + 1, 1):
|
||||
try:
|
||||
x, y, z = poly_regression(x_train, y_train, i)
|
||||
eqs.append(x)
|
||||
rmss.append(y)
|
||||
r2s.append(z)
|
||||
except:
|
||||
pass
|
||||
|
||||
for i in range(1, 100 * resolution + 1):
|
||||
try:
|
||||
x, y, z = exp_regression(x_train, y_train, float(i / resolution))
|
||||
eqs.append(x)
|
||||
rmss.append(y)
|
||||
r2s.append(z)
|
||||
except:
|
||||
pass
|
||||
|
||||
for i in range(1, 100 * resolution + 1):
|
||||
try:
|
||||
x, y, z = log_regression(x_train, y_train, float(i / resolution))
|
||||
eqs.append(x)
|
||||
rmss.append(y)
|
||||
r2s.append(z)
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
x, y, z = tanh_regression(x_train, y_train)
|
||||
|
||||
eqs.append(x)
|
||||
rmss.append(y)
|
||||
r2s.append(z)
|
||||
except:
|
||||
pass
|
||||
|
||||
# marks all equations where r2 = 1 as they 95% of the time overfit the data
|
||||
for i in range(0, len(eqs), 1):
|
||||
if r2s[i] == 1:
|
||||
eqs[i] = ""
|
||||
rmss[i] = ""
|
||||
r2s[i] = ""
|
||||
|
||||
while True: # removes all equations marked for removal
|
||||
try:
|
||||
eqs.remove('')
|
||||
rmss.remove('')
|
||||
r2s.remove('')
|
||||
except:
|
||||
break
|
||||
|
||||
overfit = []
|
||||
|
||||
for i in range(0, len(eqs), 1):
|
||||
|
||||
overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test))
|
||||
|
||||
return eqs, rmss, r2s, overfit
|
||||
|
||||
|
||||
def select_best_regression(eqs, rmss, r2s, overfit, selector):
|
||||
|
||||
b_eq = ""
|
||||
b_rms = 0
|
||||
b_r2 = 0
|
||||
b_overfit = 0
|
||||
|
||||
ind = 0
|
||||
|
||||
if selector == "min_overfit":
|
||||
|
||||
ind = np.argmin(overfit)
|
||||
|
||||
b_eq = eqs[ind]
|
||||
b_rms = rmss[ind]
|
||||
b_r2 = r2s[ind]
|
||||
b_overfit = overfit[ind]
|
||||
|
||||
if selector == "max_r2s":
|
||||
|
||||
ind = np.argmax(r2s)
|
||||
b_eq = eqs[ind]
|
||||
b_rms = rmss[ind]
|
||||
b_r2 = r2s[ind]
|
||||
b_overfit = overfit[ind]
|
||||
|
||||
return b_eq, b_rms, b_r2, b_overfit
|
||||
|
||||
|
||||
def p_value(x, y): # takes 2 1d arrays
|
||||
|
||||
return stats.ttest_ind(x, y)[1]
|
||||
|
||||
|
||||
# assumes that rows are the independent variable and columns are the dependant. also assumes that time flows from lowest column to highest column.
|
||||
def basic_analysis(data):
|
||||
|
||||
row = len(data)
|
||||
column = []
|
||||
|
||||
for i in range(0, row, 1):
|
||||
column.append(len(data[i]))
|
||||
|
||||
column_max = max(column)
|
||||
row_b_stats = []
|
||||
row_histo = []
|
||||
|
||||
for i in range(0, row, 1):
|
||||
row_b_stats.append(basic_stats(data, "row", i))
|
||||
row_histo.append(histo_analysis(data[i], 0.67449, -0.67449, 0.67449))
|
||||
|
||||
column_b_stats = []
|
||||
|
||||
for i in range(0, column_max, 1):
|
||||
column_b_stats.append(basic_stats(data, "column", i))
|
||||
|
||||
return[row_b_stats, column_b_stats, row_histo]
|
||||
|
||||
|
||||
def benchmark(x, y):
|
||||
|
||||
start_g = time.time()
|
||||
generate_data("data/data.csv", x, y, -10, 10)
|
||||
end_g = time.time()
|
||||
|
||||
start_a = time.time()
|
||||
basic_analysis("data/data.csv")
|
||||
end_a = time.time()
|
||||
|
||||
return [(end_g - start_g), (end_a - start_a)]
|
||||
|
||||
|
||||
def generate_data(filename, x, y, low, high):
|
||||
|
||||
file = open(filename, "w")
|
||||
|
||||
for i in range(0, y, 1):
|
||||
temp = ""
|
||||
|
||||
for j in range(0, x - 1, 1):
|
||||
temp = str(random.uniform(low, high)) + "," + temp
|
||||
|
||||
temp = temp + str(random.uniform(low, high))
|
||||
file.write(temp + "\n")
|
||||
|
||||
def mean(data):
|
||||
|
||||
return np.mean(data)
|
||||
|
||||
def median(data):
|
||||
|
||||
return np.median(data)
|
||||
|
||||
def mode(data):
|
||||
|
||||
return np.argmax(np.bincount(data))
|
||||
|
||||
def stdev(data):
|
||||
|
||||
return np.std(data)
|
||||
|
||||
def variance(data):
|
||||
|
||||
return np.var(data)
|
||||
|
||||
"""
|
||||
|
||||
class StatisticsError(ValueError):
|
||||
pass
|
||||
|
||||
|
||||
def _sum(data, start=0):
|
||||
count = 0
|
||||
n, d = _exact_ratio(start)
|
||||
partials = {d: n}
|
||||
partials_get = partials.get
|
||||
T = _coerce(int, type(start))
|
||||
for typ, values in groupby(data, type):
|
||||
T = _coerce(T, typ) # or raise TypeError
|
||||
for n, d in map(_exact_ratio, values):
|
||||
count += 1
|
||||
partials[d] = partials_get(d, 0) + n
|
||||
if None in partials:
|
||||
|
||||
total = partials[None]
|
||||
assert not _isfinite(total)
|
||||
else:
|
||||
|
||||
total = sum(Fraction(n, d) for d, n in sorted(partials.items()))
|
||||
return (T, total, count)
|
||||
|
||||
|
||||
def _isfinite(x):
|
||||
try:
|
||||
return x.is_finite() # Likely a Decimal.
|
||||
except AttributeError:
|
||||
return math.isfinite(x) # Coerces to float first.
|
||||
|
||||
|
||||
def _coerce(T, S):
|
||||
|
||||
assert T is not bool, "initial type T is bool"
|
||||
|
||||
if T is S:
|
||||
return T
|
||||
|
||||
if S is int or S is bool:
|
||||
return T
|
||||
if T is int:
|
||||
return S
|
||||
|
||||
if issubclass(S, T):
|
||||
return S
|
||||
if issubclass(T, S):
|
||||
return T
|
||||
|
||||
if issubclass(T, int):
|
||||
return S
|
||||
if issubclass(S, int):
|
||||
return T
|
||||
|
||||
if issubclass(T, Fraction) and issubclass(S, float):
|
||||
return S
|
||||
if issubclass(T, float) and issubclass(S, Fraction):
|
||||
return T
|
||||
|
||||
msg = "don't know how to coerce %s and %s"
|
||||
raise TypeError(msg % (T.__name__, S.__name__))
|
||||
|
||||
|
||||
def _exact_ratio(x):
|
||||
|
||||
try:
|
||||
|
||||
if type(x) is float or type(x) is Decimal:
|
||||
return x.as_integer_ratio()
|
||||
try:
|
||||
|
||||
return (x.numerator, x.denominator)
|
||||
except AttributeError:
|
||||
try:
|
||||
|
||||
return x.as_integer_ratio()
|
||||
except AttributeError:
|
||||
|
||||
pass
|
||||
except (OverflowError, ValueError):
|
||||
|
||||
assert not _isfinite(x)
|
||||
return (x, None)
|
||||
msg = "can't convert type '{}' to numerator/denominator"
|
||||
raise TypeError(msg.format(type(x).__name__))
|
||||
|
||||
|
||||
def _convert(value, T):
|
||||
|
||||
if type(value) is T:
|
||||
|
||||
return value
|
||||
if issubclass(T, int) and value.denominator != 1:
|
||||
T = float
|
||||
try:
|
||||
|
||||
return T(value)
|
||||
except TypeError:
|
||||
if issubclass(T, Decimal):
|
||||
return T(value.numerator) / T(value.denominator)
|
||||
else:
|
||||
raise
|
||||
|
||||
|
||||
def _counts(data):
|
||||
|
||||
table = collections.Counter(iter(data)).most_common()
|
||||
if not table:
|
||||
return table
|
||||
|
||||
maxfreq = table[0][1]
|
||||
for i in range(1, len(table)):
|
||||
if table[i][1] != maxfreq:
|
||||
table = table[:i]
|
||||
break
|
||||
return table
|
||||
|
||||
|
||||
def _find_lteq(a, x):
|
||||
|
||||
i = bisect_left(a, x)
|
||||
if i != len(a) and a[i] == x:
|
||||
return i
|
||||
raise ValueError
|
||||
|
||||
|
||||
def _find_rteq(a, l, x):
|
||||
|
||||
i = bisect_right(a, x, lo=l)
|
||||
if i != (len(a) + 1) and a[i - 1] == x:
|
||||
return i - 1
|
||||
raise ValueError
|
||||
|
||||
|
||||
def _fail_neg(values, errmsg='negative value'):
|
||||
|
||||
for x in values:
|
||||
if x < 0:
|
||||
raise StatisticsError(errmsg)
|
||||
yield x
|
||||
def mean(data):
|
||||
|
||||
if iter(data) is data:
|
||||
data = list(data)
|
||||
n = len(data)
|
||||
if n < 1:
|
||||
raise StatisticsError('mean requires at least one data point')
|
||||
T, total, count = _sum(data)
|
||||
assert count == n
|
||||
return _convert(total / n, T)
|
||||
|
||||
|
||||
def median(data):
|
||||
|
||||
data = sorted(data)
|
||||
n = len(data)
|
||||
if n == 0:
|
||||
raise StatisticsError("no median for empty data")
|
||||
if n % 2 == 1:
|
||||
return data[n // 2]
|
||||
else:
|
||||
i = n // 2
|
||||
return (data[i - 1] + data[i]) / 2
|
||||
|
||||
|
||||
def mode(data):
|
||||
|
||||
table = _counts(data)
|
||||
if len(table) == 1:
|
||||
return table[0][0]
|
||||
elif table:
|
||||
raise StatisticsError(
|
||||
'no unique mode; found %d equally common values' % len(table)
|
||||
)
|
||||
else:
|
||||
raise StatisticsError('no mode for empty data')
|
||||
|
||||
|
||||
def _ss(data, c=None):
|
||||
|
||||
if c is None:
|
||||
c = mean(data)
|
||||
T, total, count = _sum((x - c)**2 for x in data)
|
||||
|
||||
U, total2, count2 = _sum((x - c) for x in data)
|
||||
assert T == U and count == count2
|
||||
total -= total2**2 / len(data)
|
||||
assert not total < 0, 'negative sum of square deviations: %f' % total
|
||||
return (T, total)
|
||||
|
||||
|
||||
def variance(data, xbar=None):
|
||||
|
||||
if iter(data) is data:
|
||||
data = list(data)
|
||||
n = len(data)
|
||||
if n < 2:
|
||||
raise StatisticsError('variance requires at least two data points')
|
||||
T, ss = _ss(data, xbar)
|
||||
return _convert(ss / (n - 1), T)
|
||||
|
||||
|
||||
def stdev(data, xbar=None):
|
||||
|
||||
var = variance(data, xbar)
|
||||
try:
|
||||
return var.sqrt()
|
||||
except AttributeError:
|
||||
return math.sqrt(var)
|
||||
"""
|
||||
@ -7,10 +7,19 @@
|
||||
# number of easter eggs: 2
|
||||
# setup:
|
||||
|
||||
__version__ = "1.0.9.000"
|
||||
__version__ = "1.1.0.000"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
1.1.0.000:
|
||||
- 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
|
||||
1.0.9.000:
|
||||
- refactored
|
||||
- numpyed everything
|
||||
@ -109,26 +118,11 @@ __author__ = (
|
||||
|
||||
__all__ = [
|
||||
'_init_device',
|
||||
'c_entities',
|
||||
'nc_entities',
|
||||
'obstacles',
|
||||
'objectives',
|
||||
'load_csv',
|
||||
'basic_stats',
|
||||
'z_score',
|
||||
'z_normalize',
|
||||
'stdev_z_split',
|
||||
'histo_analysis',
|
||||
'poly_regression',
|
||||
'log_regression',
|
||||
'exp_regression',
|
||||
'r_squared',
|
||||
'rms',
|
||||
'calc_overfit',
|
||||
'strip_data',
|
||||
'optimize_regression',
|
||||
'select_best_regression',
|
||||
'basic_analysis',
|
||||
# all statistics functions left out due to integration in other functions
|
||||
]
|
||||
|
||||
@ -145,6 +139,8 @@ from fractions import Fraction
|
||||
from itertools import groupby
|
||||
import math
|
||||
import matplotlib
|
||||
import numba
|
||||
from numba import jit
|
||||
import numbers
|
||||
import numpy as np
|
||||
import pandas
|
||||
@ -152,12 +148,12 @@ import random
|
||||
import scipy
|
||||
from scipy.optimize import curve_fit
|
||||
from scipy import stats
|
||||
from sklearn import preprocessing
|
||||
from sklearn import *
|
||||
# import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
|
||||
import time
|
||||
import torch
|
||||
|
||||
|
||||
class error(ValueError):
|
||||
pass
|
||||
|
||||
@ -175,6 +171,7 @@ def _init_device(setting, arg): # initiates computation device for ANNs
|
||||
else:
|
||||
raise error("specified device does not exist")
|
||||
|
||||
@jit
|
||||
def load_csv(filepath):
|
||||
with open(filepath, newline='') as csvfile:
|
||||
file_array = np.array(list(csv.reader(csvfile)))
|
||||
@ -182,765 +179,70 @@ def load_csv(filepath):
|
||||
return file_array
|
||||
|
||||
# data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row
|
||||
def basic_stats(data, method, arg):
|
||||
@jit
|
||||
def basic_stats(data):
|
||||
|
||||
if method == 'debug':
|
||||
return "basic_stats requires 3 args: data, mode, arg; where data is data to be analyzed, mode is an int from 0 - 2 depending on type of analysis (by column or by row) and is only applicable to 2d arrays (for 1d arrays use mode 1), and arg is row/column number for mode 1 or mode 2; function returns: [mean, median, mode, stdev, variance]"
|
||||
data_t = np.array(data).astype(float)
|
||||
|
||||
if method == "1d" or method == 0:
|
||||
|
||||
data_t = np.array(data).astype(float)
|
||||
|
||||
_mean = mean(data_t)
|
||||
_median = median(data_t)
|
||||
try:
|
||||
_mode = mode(data_t)
|
||||
except:
|
||||
_mode = None
|
||||
try:
|
||||
_stdev = stdev(data_t)
|
||||
except:
|
||||
_stdev = None
|
||||
try:
|
||||
_variance = variance(data_t)
|
||||
except:
|
||||
_variance = None
|
||||
|
||||
return _mean, _median, _mode, _stdev, _variance
|
||||
"""
|
||||
elif method == "column" or method == 1:
|
||||
|
||||
c_data = []
|
||||
c_data_sorted = []
|
||||
|
||||
for i in data:
|
||||
try:
|
||||
c_data.append(float(i[arg]))
|
||||
except:
|
||||
pass
|
||||
|
||||
_mean = mean(c_data)
|
||||
_median = median(c_data)
|
||||
try:
|
||||
_mode = mode(c_data)
|
||||
except:
|
||||
_mode = None
|
||||
try:
|
||||
_stdev = stdev(c_data)
|
||||
except:
|
||||
_stdev = None
|
||||
try:
|
||||
_variance = variance(c_data)
|
||||
except:
|
||||
_variance = None
|
||||
|
||||
return _mean, _median, _mode, _stdev, _variance
|
||||
|
||||
elif method == "row" or method == 2:
|
||||
|
||||
r_data = []
|
||||
|
||||
for i in range(len(data[arg])):
|
||||
r_data.append(float(data[arg][i]))
|
||||
|
||||
_mean = mean(r_data)
|
||||
_median = median(r_data)
|
||||
try:
|
||||
_mode = mode(r_data)
|
||||
except:
|
||||
_mode = None
|
||||
try:
|
||||
_stdev = stdev(r_data)
|
||||
except:
|
||||
_stdev = None
|
||||
try:
|
||||
_variance = variance(r_data)
|
||||
except:
|
||||
_variance = None
|
||||
|
||||
return _mean, _median, _mode, _stdev, _variance
|
||||
|
||||
else:
|
||||
raise error("method error")
|
||||
"""
|
||||
_mean = mean(data_t)
|
||||
_median = median(data_t)
|
||||
_stdev = stdev(data_t)
|
||||
_variance = variance(data_t)
|
||||
|
||||
return _mean, _median, _stdev, _variance
|
||||
|
||||
# returns z score with inputs of point, mean and standard deviation of spread
|
||||
@jit
|
||||
def z_score(point, mean, stdev):
|
||||
score = (point - mean) / stdev
|
||||
return score
|
||||
|
||||
# expects 2d array, normalizes across all axes
|
||||
@jit
|
||||
def z_normalize(array, *args):
|
||||
|
||||
# mode is either 'x' or 'y' or 'both' depending on the variable(s) to be normalized
|
||||
def z_normalize(x, y, mode):
|
||||
array = np.array(array)
|
||||
|
||||
x_norm = np.array().astype(float)
|
||||
y_norm = np.array().astype(float)
|
||||
for arg in args:
|
||||
|
||||
mean = 0
|
||||
stdev = 0
|
||||
array = preprocessing.normalize(array, axis = arg)
|
||||
|
||||
if mode == 'x':
|
||||
_mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0)
|
||||
return array
|
||||
|
||||
for i in range(0, len(x), 1):
|
||||
x_norm.append(z_score(x[i], _mean, _stdev))
|
||||
@jit
|
||||
# expects 2d array of [x,y]
|
||||
def histo_analysis(hist_data):
|
||||
|
||||
return x_norm, y
|
||||
hist_data = np.array(hist_data)
|
||||
|
||||
if mode == 'y':
|
||||
_mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0)
|
||||
derivative = np.array(len(hist_data) - 1, dtype = float)
|
||||
|
||||
for i in range(0, len(y), 1):
|
||||
y_norm.append(z_score(y[i], _mean, _stdev))
|
||||
t = np.diff(hist_data)
|
||||
|
||||
return x, y_norm
|
||||
derivative = t[1] / t[0]
|
||||
|
||||
if mode == 'both':
|
||||
_mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0)
|
||||
np.sort(derivative)
|
||||
mean_derivative = basic_stats(derivative)[0]
|
||||
stdev_derivative = basic_stats(derivative)[3]
|
||||
|
||||
for i in range(0, len(x), 1):
|
||||
x_norm.append(z_score(x[i], _mean, _stdev))
|
||||
|
||||
_mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0)
|
||||
|
||||
for i in range(0, len(y), 1):
|
||||
y_norm.append(z_score(y[i], _mean, _stdev))
|
||||
|
||||
return x_norm, y_norm
|
||||
|
||||
else:
|
||||
|
||||
return error('method error')
|
||||
|
||||
|
||||
# returns n-th percentile of spread given mean, standard deviation, lower z-score, and upper z-score
|
||||
def stdev_z_split(mean, stdev, delta, low_bound, high_bound):
|
||||
|
||||
z_split = np.array().astype(float)
|
||||
i = low_bound
|
||||
|
||||
while True:
|
||||
z_split.append(float((1 / (stdev * math.sqrt(2 * math.pi))) *
|
||||
math.e ** (-0.5 * (((i - mean) / stdev) ** 2))))
|
||||
i = i + delta
|
||||
if i > high_bound:
|
||||
break
|
||||
|
||||
return z_split
|
||||
|
||||
|
||||
def histo_analysis(hist_data, delta, low_bound, high_bound):
|
||||
|
||||
if hist_data == 'debug':
|
||||
return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number of standard deviations')
|
||||
|
||||
derivative = []
|
||||
|
||||
for i in range(0, len(hist_data), 1):
|
||||
try:
|
||||
derivative.append(float(hist_data[i - 1]) - float(hist_data[i]))
|
||||
except:
|
||||
pass
|
||||
|
||||
derivative_sorted = sorted(derivative, key=int)
|
||||
mean_derivative = basic_stats(derivative_sorted, "1d", 0)[0]
|
||||
stdev_derivative = basic_stats(derivative_sorted, "1d", 0)[3]
|
||||
|
||||
predictions = []
|
||||
pred_change = 0
|
||||
|
||||
i = low_bound
|
||||
|
||||
while True:
|
||||
if i > high_bound:
|
||||
break
|
||||
|
||||
try:
|
||||
pred_change = mean_derivative + i * stdev_derivative
|
||||
except:
|
||||
pred_change = mean_derivative
|
||||
|
||||
predictions.append(float(hist_data[-1:][0]) + pred_change)
|
||||
|
||||
i = i + delta
|
||||
|
||||
return predictions
|
||||
|
||||
|
||||
def poly_regression(x, y, power):
|
||||
|
||||
if x == "null": # if x is 'null', then x will be filled with integer points between 1 and the size of y
|
||||
x = []
|
||||
|
||||
for i in range(len(y)):
|
||||
print(i)
|
||||
x.append(i + 1)
|
||||
|
||||
reg_eq = scipy.polyfit(x, y, deg=power)
|
||||
eq_str = ""
|
||||
|
||||
for i in range(0, len(reg_eq), 1):
|
||||
if i < len(reg_eq) - 1:
|
||||
eq_str = eq_str + str(reg_eq[i]) + \
|
||||
"*(z**" + str(len(reg_eq) - i - 1) + ")+"
|
||||
else:
|
||||
eq_str = eq_str + str(reg_eq[i]) + \
|
||||
"*(z**" + str(len(reg_eq) - i - 1) + ")"
|
||||
|
||||
vals = []
|
||||
|
||||
for i in range(0, len(x), 1):
|
||||
z = x[i]
|
||||
|
||||
try:
|
||||
exec("vals.append(" + eq_str + ")")
|
||||
except:
|
||||
pass
|
||||
|
||||
_rms = rms(vals, y)
|
||||
r2_d2 = r_squared(vals, y)
|
||||
|
||||
return [eq_str, _rms, r2_d2]
|
||||
|
||||
|
||||
def log_regression(x, y, base):
|
||||
|
||||
x_fit = []
|
||||
|
||||
for i in range(len(x)):
|
||||
try:
|
||||
# change of base for logs
|
||||
x_fit.append(np.log(x[i]) / np.log(base))
|
||||
except:
|
||||
pass
|
||||
|
||||
# y = reg_eq[0] * log(x, base) + reg_eq[1]
|
||||
reg_eq = np.polyfit(x_fit, y, 1)
|
||||
q_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + \
|
||||
str(base) + "))+" + str(reg_eq[1])
|
||||
vals = []
|
||||
|
||||
for i in range(len(x)):
|
||||
z = x[i]
|
||||
|
||||
try:
|
||||
exec("vals.append(" + eq_str + ")")
|
||||
except:
|
||||
pass
|
||||
|
||||
_rms = rms(vals, y)
|
||||
r2_d2 = r_squared(vals, y)
|
||||
|
||||
return eq_str, _rms, r2_d2
|
||||
|
||||
|
||||
def exp_regression(x, y, base):
|
||||
|
||||
y_fit = []
|
||||
|
||||
for i in range(len(y)):
|
||||
try:
|
||||
# change of base for logs
|
||||
y_fit.append(np.log(y[i]) / np.log(base))
|
||||
except:
|
||||
pass
|
||||
|
||||
# y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1])
|
||||
reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit))
|
||||
eq_str = "(" + str(base) + "**(" + \
|
||||
str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))"
|
||||
vals = []
|
||||
|
||||
for i in range(len(x)):
|
||||
z = x[i]
|
||||
|
||||
try:
|
||||
exec("vals.append(" + eq_str + ")")
|
||||
except:
|
||||
pass
|
||||
|
||||
_rms = rms(vals, y)
|
||||
r2_d2 = r_squared(vals, y)
|
||||
|
||||
return eq_str, _rms, r2_d2
|
||||
|
||||
|
||||
def tanh_regression(x, y):
|
||||
|
||||
def tanh(x, a, b, c, d):
|
||||
|
||||
return a * np.tanh(b * (x - c)) + d
|
||||
|
||||
reg_eq = np.float64(curve_fit(tanh, np.array(x), np.array(y))[0]).tolist()
|
||||
eq_str = str(reg_eq[0]) + " * np.tanh(" + str(reg_eq[1]) + \
|
||||
"*(z - " + str(reg_eq[2]) + ")) + " + str(reg_eq[3])
|
||||
vals = []
|
||||
|
||||
for i in range(len(x)):
|
||||
z = x[i]
|
||||
try:
|
||||
exec("vals.append(" + eq_str + ")")
|
||||
except:
|
||||
pass
|
||||
|
||||
_rms = rms(vals, y)
|
||||
r2_d2 = r_squared(vals, y)
|
||||
|
||||
return eq_str, _rms, r2_d2
|
||||
|
||||
|
||||
def r_squared(predictions, targets): # assumes equal size inputs
|
||||
|
||||
return metrics.r2_score(np.array(targets), np.array(predictions))
|
||||
|
||||
|
||||
def rms(predictions, targets): # assumes equal size inputs
|
||||
|
||||
_sum = 0
|
||||
|
||||
for i in range(0, len(targets), 1):
|
||||
_sum = (targets[i] - predictions[i]) ** 2
|
||||
|
||||
return float(math.sqrt(_sum / len(targets)))
|
||||
|
||||
|
||||
def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
|
||||
|
||||
# performance overfit = performance(train) - performance(test) where performance is r^2
|
||||
# error overfit = error(train) - error(test) where error is rms; biased towards smaller values
|
||||
|
||||
vals = []
|
||||
|
||||
for i in range(0, len(x_test), 1):
|
||||
|
||||
z = x_test[i]
|
||||
|
||||
exec("vals.append(" + equation + ")")
|
||||
|
||||
r2_test = r_squared(vals, y_test)
|
||||
rms_test = rms(vals, y_test)
|
||||
|
||||
return r2_train - r2_test
|
||||
|
||||
|
||||
def strip_data(data, mode):
|
||||
|
||||
if mode == "adam": # x is the row number, y are the data
|
||||
pass
|
||||
|
||||
if mode == "eve": # x are the data, y is the column number
|
||||
pass
|
||||
|
||||
else:
|
||||
raise error("mode error")
|
||||
|
||||
|
||||
# _range in poly regression is the range of powers tried, and in log/exp it is the inverse of the stepsize taken from -1000 to 1000
|
||||
def optimize_regression(x, y, _range, resolution):
|
||||
# usage not: for demonstration purpose only, performance is shit
|
||||
if type(resolution) != int:
|
||||
raise error("resolution must be int")
|
||||
|
||||
x_train = x
|
||||
y_train = []
|
||||
|
||||
for i in range(len(y)):
|
||||
y_train.append(float(y[i]))
|
||||
|
||||
x_test = []
|
||||
y_test = []
|
||||
|
||||
for i in range(0, math.floor(len(x) * 0.5), 1):
|
||||
index = random.randint(0, len(x) - 1)
|
||||
|
||||
x_test.append(x[index])
|
||||
y_test.append(float(y[index]))
|
||||
|
||||
x_train.pop(index)
|
||||
y_train.pop(index)
|
||||
|
||||
#print(x_train, x_test)
|
||||
#print(y_train, y_test)
|
||||
|
||||
eqs = []
|
||||
rmss = []
|
||||
r2s = []
|
||||
|
||||
for i in range(0, _range + 1, 1):
|
||||
try:
|
||||
x, y, z = poly_regression(x_train, y_train, i)
|
||||
eqs.append(x)
|
||||
rmss.append(y)
|
||||
r2s.append(z)
|
||||
except:
|
||||
pass
|
||||
|
||||
for i in range(1, 100 * resolution + 1):
|
||||
try:
|
||||
x, y, z = exp_regression(x_train, y_train, float(i / resolution))
|
||||
eqs.append(x)
|
||||
rmss.append(y)
|
||||
r2s.append(z)
|
||||
except:
|
||||
pass
|
||||
|
||||
for i in range(1, 100 * resolution + 1):
|
||||
try:
|
||||
x, y, z = log_regression(x_train, y_train, float(i / resolution))
|
||||
eqs.append(x)
|
||||
rmss.append(y)
|
||||
r2s.append(z)
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
x, y, z = tanh_regression(x_train, y_train)
|
||||
|
||||
eqs.append(x)
|
||||
rmss.append(y)
|
||||
r2s.append(z)
|
||||
except:
|
||||
pass
|
||||
|
||||
# marks all equations where r2 = 1 as they 95% of the time overfit the data
|
||||
for i in range(0, len(eqs), 1):
|
||||
if r2s[i] == 1:
|
||||
eqs[i] = ""
|
||||
rmss[i] = ""
|
||||
r2s[i] = ""
|
||||
|
||||
while True: # removes all equations marked for removal
|
||||
try:
|
||||
eqs.remove('')
|
||||
rmss.remove('')
|
||||
r2s.remove('')
|
||||
except:
|
||||
break
|
||||
|
||||
overfit = []
|
||||
|
||||
for i in range(0, len(eqs), 1):
|
||||
|
||||
overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test))
|
||||
|
||||
return eqs, rmss, r2s, overfit
|
||||
|
||||
|
||||
def select_best_regression(eqs, rmss, r2s, overfit, selector):
|
||||
|
||||
b_eq = ""
|
||||
b_rms = 0
|
||||
b_r2 = 0
|
||||
b_overfit = 0
|
||||
|
||||
ind = 0
|
||||
|
||||
if selector == "min_overfit":
|
||||
|
||||
ind = np.argmin(overfit)
|
||||
|
||||
b_eq = eqs[ind]
|
||||
b_rms = rmss[ind]
|
||||
b_r2 = r2s[ind]
|
||||
b_overfit = overfit[ind]
|
||||
|
||||
if selector == "max_r2s":
|
||||
|
||||
ind = np.argmax(r2s)
|
||||
b_eq = eqs[ind]
|
||||
b_rms = rmss[ind]
|
||||
b_r2 = r2s[ind]
|
||||
b_overfit = overfit[ind]
|
||||
|
||||
return b_eq, b_rms, b_r2, b_overfit
|
||||
|
||||
|
||||
def p_value(x, y): # takes 2 1d arrays
|
||||
|
||||
return stats.ttest_ind(x, y)[1]
|
||||
|
||||
|
||||
# assumes that rows are the independent variable and columns are the dependant. also assumes that time flows from lowest column to highest column.
|
||||
def basic_analysis(data):
|
||||
|
||||
row = len(data)
|
||||
column = []
|
||||
|
||||
for i in range(0, row, 1):
|
||||
column.append(len(data[i]))
|
||||
|
||||
column_max = max(column)
|
||||
row_b_stats = []
|
||||
row_histo = []
|
||||
|
||||
for i in range(0, row, 1):
|
||||
row_b_stats.append(basic_stats(data, "row", i))
|
||||
row_histo.append(histo_analysis(data[i], 0.67449, -0.67449, 0.67449))
|
||||
|
||||
column_b_stats = []
|
||||
|
||||
for i in range(0, column_max, 1):
|
||||
column_b_stats.append(basic_stats(data, "column", i))
|
||||
|
||||
return[row_b_stats, column_b_stats, row_histo]
|
||||
|
||||
|
||||
def benchmark(x, y):
|
||||
|
||||
start_g = time.time()
|
||||
generate_data("data/data.csv", x, y, -10, 10)
|
||||
end_g = time.time()
|
||||
|
||||
start_a = time.time()
|
||||
basic_analysis("data/data.csv")
|
||||
end_a = time.time()
|
||||
|
||||
return [(end_g - start_g), (end_a - start_a)]
|
||||
|
||||
|
||||
def generate_data(filename, x, y, low, high):
|
||||
|
||||
file = open(filename, "w")
|
||||
|
||||
for i in range(0, y, 1):
|
||||
temp = ""
|
||||
|
||||
for j in range(0, x - 1, 1):
|
||||
temp = str(random.uniform(low, high)) + "," + temp
|
||||
|
||||
temp = temp + str(random.uniform(low, high))
|
||||
file.write(temp + "\n")
|
||||
return mean_derivative, stdev_derivative
|
||||
|
||||
@jit
|
||||
def mean(data):
|
||||
|
||||
return np.mean(data)
|
||||
|
||||
@jit
|
||||
def median(data):
|
||||
|
||||
return np.median(data)
|
||||
|
||||
def mode(data):
|
||||
|
||||
return np.argmax(np.bincount(data))
|
||||
|
||||
@jit
|
||||
def stdev(data):
|
||||
|
||||
return np.std(data)
|
||||
|
||||
@jit
|
||||
def variance(data):
|
||||
|
||||
return np.var(data)
|
||||
|
||||
"""
|
||||
|
||||
class StatisticsError(ValueError):
|
||||
pass
|
||||
|
||||
|
||||
def _sum(data, start=0):
|
||||
count = 0
|
||||
n, d = _exact_ratio(start)
|
||||
partials = {d: n}
|
||||
partials_get = partials.get
|
||||
T = _coerce(int, type(start))
|
||||
for typ, values in groupby(data, type):
|
||||
T = _coerce(T, typ) # or raise TypeError
|
||||
for n, d in map(_exact_ratio, values):
|
||||
count += 1
|
||||
partials[d] = partials_get(d, 0) + n
|
||||
if None in partials:
|
||||
|
||||
total = partials[None]
|
||||
assert not _isfinite(total)
|
||||
else:
|
||||
|
||||
total = sum(Fraction(n, d) for d, n in sorted(partials.items()))
|
||||
return (T, total, count)
|
||||
|
||||
|
||||
def _isfinite(x):
|
||||
try:
|
||||
return x.is_finite() # Likely a Decimal.
|
||||
except AttributeError:
|
||||
return math.isfinite(x) # Coerces to float first.
|
||||
|
||||
|
||||
def _coerce(T, S):
|
||||
|
||||
assert T is not bool, "initial type T is bool"
|
||||
|
||||
if T is S:
|
||||
return T
|
||||
|
||||
if S is int or S is bool:
|
||||
return T
|
||||
if T is int:
|
||||
return S
|
||||
|
||||
if issubclass(S, T):
|
||||
return S
|
||||
if issubclass(T, S):
|
||||
return T
|
||||
|
||||
if issubclass(T, int):
|
||||
return S
|
||||
if issubclass(S, int):
|
||||
return T
|
||||
|
||||
if issubclass(T, Fraction) and issubclass(S, float):
|
||||
return S
|
||||
if issubclass(T, float) and issubclass(S, Fraction):
|
||||
return T
|
||||
|
||||
msg = "don't know how to coerce %s and %s"
|
||||
raise TypeError(msg % (T.__name__, S.__name__))
|
||||
|
||||
|
||||
def _exact_ratio(x):
|
||||
|
||||
try:
|
||||
|
||||
if type(x) is float or type(x) is Decimal:
|
||||
return x.as_integer_ratio()
|
||||
try:
|
||||
|
||||
return (x.numerator, x.denominator)
|
||||
except AttributeError:
|
||||
try:
|
||||
|
||||
return x.as_integer_ratio()
|
||||
except AttributeError:
|
||||
|
||||
pass
|
||||
except (OverflowError, ValueError):
|
||||
|
||||
assert not _isfinite(x)
|
||||
return (x, None)
|
||||
msg = "can't convert type '{}' to numerator/denominator"
|
||||
raise TypeError(msg.format(type(x).__name__))
|
||||
|
||||
|
||||
def _convert(value, T):
|
||||
|
||||
if type(value) is T:
|
||||
|
||||
return value
|
||||
if issubclass(T, int) and value.denominator != 1:
|
||||
T = float
|
||||
try:
|
||||
|
||||
return T(value)
|
||||
except TypeError:
|
||||
if issubclass(T, Decimal):
|
||||
return T(value.numerator) / T(value.denominator)
|
||||
else:
|
||||
raise
|
||||
|
||||
|
||||
def _counts(data):
|
||||
|
||||
table = collections.Counter(iter(data)).most_common()
|
||||
if not table:
|
||||
return table
|
||||
|
||||
maxfreq = table[0][1]
|
||||
for i in range(1, len(table)):
|
||||
if table[i][1] != maxfreq:
|
||||
table = table[:i]
|
||||
break
|
||||
return table
|
||||
|
||||
|
||||
def _find_lteq(a, x):
|
||||
|
||||
i = bisect_left(a, x)
|
||||
if i != len(a) and a[i] == x:
|
||||
return i
|
||||
raise ValueError
|
||||
|
||||
|
||||
def _find_rteq(a, l, x):
|
||||
|
||||
i = bisect_right(a, x, lo=l)
|
||||
if i != (len(a) + 1) and a[i - 1] == x:
|
||||
return i - 1
|
||||
raise ValueError
|
||||
|
||||
|
||||
def _fail_neg(values, errmsg='negative value'):
|
||||
|
||||
for x in values:
|
||||
if x < 0:
|
||||
raise StatisticsError(errmsg)
|
||||
yield x
|
||||
def mean(data):
|
||||
|
||||
if iter(data) is data:
|
||||
data = list(data)
|
||||
n = len(data)
|
||||
if n < 1:
|
||||
raise StatisticsError('mean requires at least one data point')
|
||||
T, total, count = _sum(data)
|
||||
assert count == n
|
||||
return _convert(total / n, T)
|
||||
|
||||
|
||||
def median(data):
|
||||
|
||||
data = sorted(data)
|
||||
n = len(data)
|
||||
if n == 0:
|
||||
raise StatisticsError("no median for empty data")
|
||||
if n % 2 == 1:
|
||||
return data[n // 2]
|
||||
else:
|
||||
i = n // 2
|
||||
return (data[i - 1] + data[i]) / 2
|
||||
|
||||
|
||||
def mode(data):
|
||||
|
||||
table = _counts(data)
|
||||
if len(table) == 1:
|
||||
return table[0][0]
|
||||
elif table:
|
||||
raise StatisticsError(
|
||||
'no unique mode; found %d equally common values' % len(table)
|
||||
)
|
||||
else:
|
||||
raise StatisticsError('no mode for empty data')
|
||||
|
||||
|
||||
def _ss(data, c=None):
|
||||
|
||||
if c is None:
|
||||
c = mean(data)
|
||||
T, total, count = _sum((x - c)**2 for x in data)
|
||||
|
||||
U, total2, count2 = _sum((x - c) for x in data)
|
||||
assert T == U and count == count2
|
||||
total -= total2**2 / len(data)
|
||||
assert not total < 0, 'negative sum of square deviations: %f' % total
|
||||
return (T, total)
|
||||
|
||||
|
||||
def variance(data, xbar=None):
|
||||
|
||||
if iter(data) is data:
|
||||
data = list(data)
|
||||
n = len(data)
|
||||
if n < 2:
|
||||
raise StatisticsError('variance requires at least two data points')
|
||||
T, ss = _ss(data, xbar)
|
||||
return _convert(ss / (n - 1), T)
|
||||
|
||||
|
||||
def stdev(data, xbar=None):
|
||||
|
||||
var = variance(data, xbar)
|
||||
try:
|
||||
return var.sqrt()
|
||||
except AttributeError:
|
||||
return math.sqrt(var)
|
||||
"""
|
||||
|
||||
Loading…
Reference in New Issue
Block a user