From f49bb58215d93accb6695c5f282438d3f13e9539 Mon Sep 17 00:00:00 2001 From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com> Date: Fri, 5 Apr 2019 17:24:24 -0500 Subject: [PATCH] started c-ifying analysis --- data analysis/analysis.py | 509 ++++++++++-------- .../Release/analysis.cp37-win_amd64.exp | Bin 0 -> 779 bytes .../Release/analysis.cp37-win_amd64.lib | Bin 0 -> 2016 bytes .../temp.win-amd64-3.7/Release/analysis.obj | Bin 0 -> 2145802 bytes .../Release/c_analysis.cp37-win_amd64.exp | Bin 0 -> 789 bytes .../Release/c_analysis.cp37-win_amd64.lib | Bin 0 -> 2052 bytes .../temp.win-amd64-3.7/Release/c_analysis.obj | Bin 0 -> 2134095 bytes data analysis/setup.py | 5 + 8 files changed, 290 insertions(+), 224 deletions(-) create mode 100644 data analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.exp create mode 100644 data analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.lib create mode 100644 data analysis/build/temp.win-amd64-3.7/Release/analysis.obj create mode 100644 data analysis/build/temp.win-amd64-3.7/Release/c_analysis.cp37-win_amd64.exp create mode 100644 data analysis/build/temp.win-amd64-3.7/Release/c_analysis.cp37-win_amd64.lib create mode 100644 data analysis/build/temp.win-amd64-3.7/Release/c_analysis.obj create mode 100644 data analysis/setup.py diff --git a/data analysis/analysis.py b/data analysis/analysis.py index e9fe227c..e226d219 100644 --- a/data analysis/analysis.py +++ b/data analysis/analysis.py @@ -1,15 +1,15 @@ -#Titan Robotics Team 2022: Data Analysis Module -#Written by Arthur Lu & Jacob Levine -#Notes: +# Titan Robotics Team 2022: Data Analysis Module +# Written by Arthur Lu & Jacob Levine +# Notes: # this should be imported as a python module using 'import analysis' # this should be included in the local directory or environment variable # this module has not been optimized for multhreaded computing -#number of easter eggs: 2 -#setup: +# number of easter eggs: 2 +# setup: __version__ = "1.0.8.005" -#changelog should be viewed using print(analysis.__changelog__) +# changelog should be viewed using print(analysis.__changelog__) __changelog__ = """changelog: 1.0.8.005: - minor fixes @@ -96,12 +96,12 @@ __changelog__ = """changelog: 1.0.0.xxx: - added loading csv - added 1d, column, row basic stats -""" +""" __author__ = ( "Arthur Lu , " "Jacob Levine ," - ) +) __all__ = [ '_init_device', @@ -125,12 +125,12 @@ __all__ = [ 'optimize_regression', 'select_best_regression', 'basic_analysis', - #all statistics functions left out due to integration in other functions - ] + # all statistics functions left out due to integration in other functions +] -#now back to your regularly scheduled programming: +# now back to your regularly scheduled programming: -#imports (now in alphabetical order! v 1.0.3.006): +# imports (now in alphabetical order! v 1.0.3.006): from bisect import bisect_left, bisect_right import collections @@ -149,16 +149,18 @@ import scipy from scipy.optimize import curve_fit from scipy import stats from sklearn import * -#import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002 +# 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 -def _init_device (setting, arg): #initiates computation device for ANNs + +def _init_device(setting, arg): # initiates computation device for ANNs if setting == "cuda": - try: + try: return torch.device(setting + ":" + str(arg) if torch.cuda.is_available() else "cpu") except: raise error("could not assign cuda or cpu") @@ -170,6 +172,7 @@ def _init_device (setting, arg): #initiates computation device for ANNs else: raise error("specified device does not exist") + class c_entities: c_names = [] @@ -181,7 +184,7 @@ class c_entities: def debug(self): print("c_entities has attributes names, ids, positions, properties, and logic. __init__ takes self, 1d array of names, 1d array of ids, 2d array of positions, nd array of properties, and nd array of logic") return[self.c_names, self.c_ids, self.c_pos, self.c_properties, self.c_logic] - + def __init__(self, names, ids, pos, properties, logic): self.c_names = names self.c_ids = ids @@ -189,7 +192,6 @@ class c_entities: self.c_properties = properties self.c_logic = logic return None - def append(self, n_name, n_id, n_pos, n_property, n_logic): self.c_names.append(n_name) @@ -198,7 +200,7 @@ class c_entities: self.c_properties.append(n_property) self.c_logic.append(n_logic) return None - + def edit(self, search, n_name, n_id, n_pos, n_property, n_logic): position = 0 for i in range(0, len(self.c_ids), 1): @@ -206,32 +208,33 @@ class c_entities: position = i if n_name != "null": self.c_names[position] = n_name - + if n_id != "null": self.c_ids[position] = n_id - + if n_pos != "null": self.c_pos[position] = n_pos - + if n_property != "null": self.c_properties[position] = n_property - + if n_logic != "null": self.c_logic[position] = n_logic - + return None - + def search(self, search): position = 0 for i in range(0, len(self.c_ids), 1): if self.c_ids[i] == search: position = i - return [self.c_names[position], self.c_ids[position], self.c_pos[position], self.c_properties[position], self.c_logic[position]] + return [self.c_names[position], self.c_ids[position], self.c_pos[position], self.c_properties[position], self.c_logic[position]] def regurgitate(self): return[self.c_names, self.c_ids, self.c_pos, self.c_properties, self.c_logic] - + + class nc_entities: c_names = [] @@ -241,7 +244,7 @@ class nc_entities: c_effects = [] def debug(self): - print ("nc_entities (non-controlable entities) has attributes names, ids, positions, properties, and effects. __init__ takes self, 1d array of names, 1d array of ids, 2d array of positions, 2d array of properties, and 2d array of effects.") + print("nc_entities (non-controlable entities) has attributes names, ids, positions, properties, and effects. __init__ takes self, 1d array of names, 1d array of ids, 2d array of positions, 2d array of properties, and 2d array of effects.") return[self.c_names, self.c_ids, self.c_pos, self.c_properties, self.c_effects] def __init__(self, names, ids, pos, properties, effects): @@ -258,7 +261,7 @@ class nc_entities: self.c_pos.append(n_pos) self.c_properties.append(n_property) self.c_effects.append(n_effect) - + return None def edit(self, search, n_name, n_id, n_pos, n_property, n_effect): @@ -289,12 +292,13 @@ class nc_entities: if self.c_ids[i] == search: position = i - return [self.c_names[position], self.c_ids[position], self.c_pos[position], self.c_properties[position], self.c_effects[position]] + return [self.c_names[position], self.c_ids[position], self.c_pos[position], self.c_properties[position], self.c_effects[position]] def regurgitate(self): return[self.c_names, self.c_ids, self.c_pos, self.c_properties, self.c_effects] + class obstacles: c_names = [] @@ -337,7 +341,7 @@ class obstacles: if n_effect != "null": self.c_effects[position] = n_effect - + return None def search(self, search): @@ -351,8 +355,9 @@ class obstacles: def regurgitate(self): return[self.c_names, self.c_ids, self.c_perim, self.c_effects] + class objectives: - + c_names = [] c_ids = [] c_pos = [] @@ -361,21 +366,21 @@ class objectives: def debug(self): print("objectives has atributes names, ids, positions, and effects. __init__ takes self, 1d array of names, 1d array of ids, 2d array of position, 1d array of effects.") return [self.c_names, self.c_ids, self.c_pos, self.c_effects] - + def __init__(self, names, ids, pos, effects): self.c_names = names self.c_ids = ids self.c_pos = pos self.c_effects = effects return None - + def append(self, n_name, n_id, n_pos, n_effect): self.c_names.append(n_name) self.c_ids.append(n_id) self.c_pos.append(n_pos) self.c_effects.append(n_effect) return None - + def edit(self, search, n_name, n_id, n_pos, n_effect): position = 0 print(self.c_ids) @@ -394,9 +399,9 @@ class objectives: if n_effect != "null": self.c_effects[position] = n_effect - + return None - + def search(self, search): position = 0 for i in range(0, len(self.c_ids), 1): @@ -407,15 +412,18 @@ class objectives: def regurgitate(self): return[self.c_names, self.c_ids, self.c_pos, self.c_effects] - + + def load_csv(filepath): - with open(filepath, newline = '') as csvfile: + with open(filepath, newline='') as csvfile: file_array = list(csv.reader(csvfile)) csvfile.close() return file_array -def basic_stats(data, method, arg): # 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 - + +# 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): + 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]" @@ -423,9 +431,9 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, ' data_t = [] - for i in range (0, len(data), 1): + for i in range(0, len(data), 1): data_t.append(float(data[i])) - + _mean = mean(data_t) _median = median(data_t) try: @@ -433,7 +441,7 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, ' except: _mode = None try: - _stdev = stdev(data_t) + _stdev = stdev(data_t) except: _stdev = None try: @@ -442,18 +450,18 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, ' _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: @@ -477,7 +485,7 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, ' for i in range(len(data[arg])): r_data.append(float(data[arg][i])) - + _mean = mean(r_data) _median = median(r_data) try: @@ -492,70 +500,78 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, ' _variance = variance(r_data) except: _variance = None - + return _mean, _median, _mode, _stdev, _variance else: raise error("method error") - -def z_score(point, mean, stdev): #returns z score with inputs of point, mean and standard deviation of spread - score = (point - mean)/stdev + + +# returns z score with inputs of point, mean and standard deviation of spread +def z_score(point, mean, stdev): + score = (point - mean) / stdev return score -def z_normalize(x, y, mode): #mode is either 'x' or 'y' or 'both' depending on the variable(s) to be normalized - x_norm = [] - y_norm = [] +# mode is either 'x' or 'y' or 'both' depending on the variable(s) to be normalized +def z_normalize(x, y, mode): - mean = 0 - stdev = 0 + x_norm = [] + y_norm = [] - if mode == 'x': - _mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0) + mean = 0 + stdev = 0 - for i in range (0, len(x), 1): - x_norm.append(z_score(x[i], _mean, _stdev)) + if mode == 'x': + _mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0) - return x_norm, y + for i in range(0, len(x), 1): + x_norm.append(z_score(x[i], _mean, _stdev)) - if mode == 'y': - _mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0) + return x_norm, y - for i in range (0, len(y), 1): - y_norm.append(z_score(y[i], _mean, _stdev)) + if mode == 'y': + _mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0) - return x, y_norm + for i in range(0, len(y), 1): + y_norm.append(z_score(y[i], _mean, _stdev)) - if mode == 'both': - _mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0) + return x, y_norm - for i in range (0, len(x), 1): - x_norm.append(z_score(x[i], _mean, _stdev)) + if mode == 'both': + _mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0) - _mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0) + for i in range(0, len(x), 1): + x_norm.append(z_score(x[i], _mean, _stdev)) - for i in range (0, len(y), 1): - y_norm.append(z_score(y[i], _mean, _stdev)) + _mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0) - return x_norm, y_norm + for i in range(0, len(y), 1): + y_norm.append(z_score(y[i], _mean, _stdev)) - else: + return x_norm, y_norm - return error('method error') + else: -def stdev_z_split(mean, stdev, delta, low_bound, high_bound): #returns n-th percentile of spread given mean, standard deviation, lower z-score, and upper z-score + 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 = [] i = low_bound while True: - z_split.append(float((1 / (stdev * math.sqrt(2 * math.pi))) * math.e ** (-0.5 * (((i - mean) / stdev) ** 2)))) + 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': @@ -565,12 +581,12 @@ def histo_analysis(hist_data, delta, low_bound, high_bound): for i in range(0, len(hist_data), 1): try: - derivative.append(float(hist_data[i - 1]) - float(hist_data [i])) + 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] + mean_derivative = basic_stats(derivative_sorted, "1d", 0)[0] stdev_derivative = basic_stats(derivative_sorted, "1d", 0)[3] predictions = [] @@ -584,7 +600,7 @@ def histo_analysis(hist_data, delta, low_bound, high_bound): try: pred_change = mean_derivative + i * stdev_derivative - except: + except: pred_change = mean_derivative predictions.append(float(hist_data[-1:][0]) + pred_change) @@ -593,23 +609,26 @@ def histo_analysis(hist_data, delta, low_bound, high_bound): 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 + 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) + x.append(i + 1) - reg_eq = scipy.polyfit(x, y, deg = power) + 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) + ")+" + 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) + ")" + eq_str = eq_str + str(reg_eq[i]) + \ + "*(z**" + str(len(reg_eq) - i - 1) + ")" vals = [] @@ -617,108 +636,121 @@ def poly_regression(x, y, power): z = x[i] try: - exec("vals.append(" + eq_str + ")") + exec("vals.append(" + eq_str + ")") except: - pass + pass _rms = rms(vals, y) - r2_d2 = r_squared(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: - x_fit.append(np.log(x[i]) / np.log(base)) #change of base for logs - except: - pass + x_fit = [] - reg_eq = np.polyfit(x_fit, y, 1) # y = reg_eq[0] * log(x, base) + reg_eq[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)): + try: + # change of base for logs + x_fit.append(np.log(x[i]) / np.log(base)) + except: + pass - for i in range(len(x)): - z = x[i] + # 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 = [] - try: - exec("vals.append(" + eq_str + ")") - except: - pass + for i in range(len(x)): + z = x[i] - _rms = rms(vals, y) - r2_d2 = r_squared(vals, y) + try: + exec("vals.append(" + eq_str + ")") + except: + pass + + _rms = rms(vals, y) + r2_d2 = r_squared(vals, y) + + return eq_str, _rms, r2_d2 - return eq_str, _rms, r2_d2 def exp_regression(x, y, base): - y_fit = [] + y_fit = [] - for i in range(len(y)): - try: - y_fit.append(np.log(y[i]) / np.log(base)) #change of base for logs - except: - pass + for i in range(len(y)): + try: + # change of base for logs + y_fit.append(np.log(y[i]) / np.log(base)) + except: + pass - reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit)) # y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1]) - eq_str = "(" + str(base) + "**(" + str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))" - vals = [] + # 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] + for i in range(len(x)): + z = x[i] - try: - exec("vals.append(" + eq_str + ")") - except: - pass + try: + exec("vals.append(" + eq_str + ")") + except: + pass - _rms = rms(vals, y) - r2_d2 = r_squared(vals, y) + _rms = rms(vals, y) + r2_d2 = r_squared(vals, y) + + return eq_str, _rms, r2_d2 - return eq_str, _rms, r2_d2 def tanh_regression(x, y): - def tanh (x, a, b, c, d): + def tanh(x, a, b, c, d): - return a * np.tanh(b * (x - 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 = [] + 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 + 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) + _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 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 + +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))) + 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 + # 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 = [] @@ -733,19 +765,22 @@ def calc_overfit(equation, rms_train, r2_train, x_test, y_test): return r2_train - r2_test + def strip_data(data, mode): - if mode == "adam": #x is the row number, y are the data + 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 + if mode == "eve": # x are the data, y is the column number pass else: raise error("mode error") -def optimize_regression(x, y, _range, resolution):#_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 -#usage not: for demonstration purpose only, performance is shit + +# _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") @@ -758,7 +793,7 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is x_test = [] y_test = [] - for i in range (0, math.floor(len(x) * 0.5), 1): + for i in range(0, math.floor(len(x) * 0.5), 1): index = random.randint(0, len(x) - 1) x_test.append(x[index]) @@ -769,12 +804,12 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is #print(x_train, x_test) #print(y_train, y_test) - + eqs = [] rmss = [] r2s = [] - for i in range (0, _range + 1, 1): + for i in range(0, _range + 1, 1): try: x, y, z = poly_regression(x_train, y_train, i) eqs.append(x) @@ -783,21 +818,21 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is except: pass - for i in range (1, 100 * resolution + 1): + 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) + 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): + 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) + x, y, z = log_regression(x_train, y_train, float(i / resolution)) + eqs.append(x) + rmss.append(y) + r2s.append(z) except: pass @@ -809,15 +844,16 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is r2s.append(z) except: pass - - for i in range (0, len(eqs), 1): #marks all equations where r2 = 1 as they 95% of the time overfit the data + + # 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: + while True: # removes all equations marked for removal + try: eqs.remove('') rmss.remove('') r2s.remove('') @@ -826,12 +862,13 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is overfit = [] - for i in range (0, len(eqs), 1): + 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 = "" @@ -860,37 +897,40 @@ def select_best_regression(eqs, rmss, r2s, overfit, selector): 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] -def basic_analysis(data): #assumes that rows are the independent variable and columns are the dependant. also assumes that time flows from lowest column to highest column. +def p_value(x, y): # takes 2 1d arrays - 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] + 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() + start_g = time.time() generate_data("data/data.csv", x, y, -10, 10) end_g = time.time() @@ -900,22 +940,25 @@ def benchmark(x, y): 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): + for i in range(0, y, 1): temp = "" - - for j in range (0, x - 1, 1): - temp = str(random.uniform(low, high)) + "," + 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") + class StatisticsError(ValueError): pass + def _sum(data, start=0): count = 0 n, d = _exact_ratio(start) @@ -924,7 +967,7 @@ def _sum(data, start=0): 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): + for n, d in map(_exact_ratio, values): count += 1 partials[d] = partials_get(d, 0) + n if None in partials: @@ -936,26 +979,35 @@ def _sum(data, start=0): 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 T is S: + return T - if S is int or S is bool: return T - if T is int: return S + 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(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, int): + return S + if issubclass(S, int): + return T if issubclass(T, Fraction) and issubclass(S, float): return S @@ -965,6 +1017,7 @@ def _coerce(T, S): msg = "don't know how to coerce %s and %s" raise TypeError(msg % (T.__name__, S.__name__)) + def _exact_ratio(x): try: @@ -988,6 +1041,7 @@ def _exact_ratio(x): msg = "can't convert type '{}' to numerator/denominator" raise TypeError(msg.format(type(x).__name__)) + def _convert(value, T): if type(value) is T: @@ -1000,10 +1054,11 @@ def _convert(value, T): return T(value) except TypeError: if issubclass(T, Decimal): - return T(value.numerator)/T(value.denominator) + return T(value.numerator) / T(value.denominator) else: raise + def _counts(data): table = collections.Counter(iter(data)).most_common() @@ -1029,8 +1084,8 @@ def _find_lteq(a, x): 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 + if i != (len(a) + 1) and a[i - 1] == x: + return i - 1 raise ValueError @@ -1041,6 +1096,7 @@ def _fail_neg(values, errmsg='negative value'): raise StatisticsError(errmsg) yield x + def mean(data): if iter(data) is data: @@ -1050,44 +1106,48 @@ def mean(data): raise StatisticsError('mean requires at least one data point') T, total, count = _sum(data) assert count == n - return _convert(total/n, T) + 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] + if n % 2 == 1: + return data[n // 2] else: - i = n//2 - return (data[i - 1] + data[i])/2 + 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) - ) + '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) + T, total, count = _sum((x - c)**2 for x in data) - U, total2, count2 = _sum((x-c) 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) + 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: @@ -1096,7 +1156,8 @@ def variance(data, xbar=None): if n < 2: raise StatisticsError('variance requires at least two data points') T, ss = _ss(data, xbar) - return _convert(ss/(n-1), T) + return _convert(ss / (n - 1), T) + def stdev(data, xbar=None): diff --git a/data analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.exp b/data 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