From f665a68245b5d62875632871310ce33ecf970f66 Mon Sep 17 00:00:00 2001 From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com> Date: Tue, 6 Nov 2018 22:08:43 -0600 Subject: [PATCH] commit --- __pycache__/analysis.cpython-37.pyc | Bin 8361 -> 8544 bytes __pycache__/generate_data.cpython-37.pyc | Bin 498 -> 483 bytes analysis.py | 339 +---------------------- analysis_test.py | 16 +- data.txt | 49 ++++ 5 files changed, 51 insertions(+), 353 deletions(-) create mode 100644 data.txt diff --git a/__pycache__/analysis.cpython-37.pyc b/__pycache__/analysis.cpython-37.pyc index c645d4e8834b7c0007e2fb032b9251be4f75633f..01dbc7295d363be9b999971ba170ffda3fadd817 100644 GIT binary patch delta 2506 zcmb7GOKclO7~b)7{n|K*<40^aN#hW?4I~ZeD~ggdrb9kyhK?t+UkiS~HuV z1ho-Rai}V!Fs;M^%7p_J0ws!&P$k5PD=HTbd#DOh4{)L4h(KcgG4;l7(uXa78qNIw z_y6;M|IGN#u?G{rxWkSnoX*o)I)r3tSVlzz44N)&2WoTH7_D z5Yf;%As_!89OhKO#~;K7D5R&RTK(-ZtJpxs;rkS!!mR-mltW!pQH1xgi#2q 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b4dda5a2d6e5bfcf668850b024d875c80079674f..a304ff14d3a405a70d34bbdcb6c0cc0d606772df 100644 GIT binary patch delta 53 zcmeyw{Fs^BiIm;(Ts6ctMV diff --git a/analysis.py b/analysis.py index 2ec9c306..27fa4d61 100644 --- a/analysis.py +++ b/analysis.py @@ -1,4 +1,4 @@ -<<<<<<< HEAD + #this should be imported as a python module using 'import analysis' import statistics @@ -334,340 +334,3 @@ def histo_analysis_2(hist_data, delta, low_bound, high_bound): break return predictions -======= -#this should be imported as a python module using 'import analysis' - -import statistics -import math -import csv -import functools - -class c_entities: - - c_names = [] - c_ids = [] - c_pos = [] - c_porperties = [] - c_logic = [] - -class nc_entities: - - c_names = [] - c_ids = [] - c_pos = [] - c_properties = [] - 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 psoitions, 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): - self.c_names = names - self.c_ids = ids - self.c_pos = pos - self.c_properties = properties - self.c_effects = effects - return None - - def append(self, n_name, n_id, n_pos, n_property, n_effect): - self.c_names.append(n_name) - self.c_ids.append(n_id) - self.c_pos.append(n_pos) - self.c_properties.append(n_property) - self.c_effects.append(n_effect) - - def edit(self, search, n_name, n_id, n_pos, n_property, n_effect): - position = 0 - for i in range(0, len(self.c_ids), 1): - if self.c_ids[i] == search: - 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_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): - 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]] - -class obstacles: - - c_names = [] - c_ids = [] - c_perim = [] - c_effects = [] - - def debug(self): - print("obstacles has atributes names, ids, positions, perimeters, and effects. __init__ takes self, 1d array of names, 1d array of ids, 2d array of position, 3d array of perimeters, 2d array of effects.") - return [self.c_names, self.c_ids, self.c_perim, self.c_effects] - - def __init__(self, names, ids, perims, effects): - self.c_names = names - self.c_ids = ids - self.c_perim = perims - self.c_effects = effects - return None - - def append(self, n_name, n_id, n_perim, n_effect): - self.c_names.append(n_name) - self.c_ids.append(n_id) - self.c_perim.append(n_perim) - self.c_effects.append(n_effect) - return None - - def edit(self, search, n_name, n_id, n_perim, n_effect): - position = 0 - for i in range(0, len(self.c_ids), 1): - if self.c_ids[i] == search: - position = i - - if n_name != "null": - self.c_names[position] = n_name - - if n_id != "null": - self.c_ids[position] = n_id - - if n_perim != "null": - self.c_perim[position] = n_perim - - 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): - if self.c_ids[i] == search: - position = i - - return [self.c_names[position], self.c_ids[position], self.c_perim[position], self.c_effects[position]] - -class objectives: - - c_names = [] - c_ids = [] - c_pos = [] - c_effects = [] - - 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) - for i in range(0, len(self.c_ids), 1): - if self.c_ids[i] == search: - 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_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): - if self.c_ids[i] == search: - position = i - - return [self.c_names[position], self.c_ids[position], self.c_pos[position], self.c_effects[position]] - -def load_csv(filepath): - with open(filepath, newline = '') as csvfile: - file_array = list(csv.reader(csvfile)) - return file_array - -def basic_stats(data, mode, 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 - - if mode == 'debug': - out = "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]" - return out - - if mode == "1d" or mode == 0: - - data_t = [] - - for i in range (0, len(data) - 1, 1): - - data_t.append(float(data[i])) - - mean = statistics.mean(data_t) - median = statistics.median(data_t) - try: - mode = statistics.mode(data_t) - except: - mode = None - stdev = statistics.stdev(data_t) - variance = statistics.variance(data_t) - - out = [mean, median, mode, stdev, variance] - - return out - - elif mode == "column" or mode == 1: - - c_data = [] - c_data_sorted = [] - - for i in data: - c_data.append(float(i[arg])) - - mean = statistics.mean(c_data) - median = statistics.median(c_data) - try: - mode = statistics.mode(c_data) - except: - mode = None - stdev = statistics.stdev(c_data) - variance = statistics.variance(c_data) - - out = [mean, median, mode, stdev, variance] - - return out - - elif mode == "row" or mode == 2: - - r_data = [] - - for i in range(len(data[arg])): - r_data.append(float(data[arg][i])) - - mean = statistics.mean(r_data) - median = statistics.median(r_data) - try: - mode = statistics.mode(r_data) - except: - mode = None - stdev = statistics.stdev(r_data) - variance = statistics.variance(r_data) - - out = [mean, median, mode, stdev, variance] - - return out - else: - return ["mode_error", "mode_error"] - -def z_score(point, mean, stdev): - score = (point - mean)/stdev - return 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)))) - - i = i + delta - - if i > high_bound: - - break - - return z_split - -def histo_analysis(hist_data): #note: depreciated - - if hist_data == 'debug': - return['lower estimate (5%)', 'lower middle estimate (25%)', 'middle estimate (50%)', 'higher middle estimate (75%)', 'high estimate (95%)', 'standard deviation', 'note: this has been depreciated'] - - derivative = [] - for i in range(0, len(hist_data) - 1, 1): - derivative.append(float(hist_data[i+1]) - float(hist_data[i])) - - derivative_sorted = sorted(derivative, key=int) - mean_derivative = basic_stats(derivative_sorted, "1d", 0)[0] - stdev_derivative = basic_stats(derivative_sorted, "1d", 0)[3] - - low_bound = mean_derivative + -1.645 * stdev_derivative - lm_bound = mean_derivative + -0.674 * stdev_derivative - mid_bound = mean_derivative * 0 * stdev_derivative - hm_bound = mean_derivative + 0.674 * stdev_derivative - high_bound = mean_derivative + 1.645 * stdev_derivative - - low_est = float(hist_data[-1:][0]) + low_bound - lm_est = float(hist_data[-1:][0]) + lm_bound - mid_est = float(hist_data[-1:][0]) + mid_bound - hm_est = float(hist_data[-1:][0]) + hm_bound - high_est = float(hist_data[-1:][0]) + high_bound - - return [low_est, lm_est, mid_est, hm_est, high_est, stdev_derivative] - -def histo_analysis_2(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 igher bounds in number for standard deviations') - - derivative = [] - - for i in range(0, len(hist_data) - 1, 1): - derivative.append(float(hist_data[i + 1]) - float(hist_data [i])) - - 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: - - pred_change = mean_derivative + i * stdev_derivative - - predictions.append(float(hist_data[-1:][0]) + pred_change) - - i = i + delta - - if i > high_bound: - - break - - return predictions ->>>>>>> 18189b45b17228228e192c270c12c3cc2cb7f8cf diff --git a/analysis_test.py b/analysis_test.py index f9f34f2c..cf2ffd21 100644 --- a/analysis_test.py +++ b/analysis_test.py @@ -1,4 +1,4 @@ -<<<<<<< HEAD + import analysis data = analysis.load_csv('data.txt') @@ -27,17 +27,3 @@ game_objectives.append("auto", 3, [0, 10], "1, 10") game_objectives.edit(3, "null", 4, "null", "null") print(game_objectives.search(4)) print(game_objectives.debug()) -======= -import analysis - -data = analysis.load_csv('data.txt') -print(analysis.basic_stats(0, 'debug', 0)) -print(analysis.basic_stats(data, 1, 0)) -print(analysis.basic_stats(data, 2, 0)) -print(analysis.z_score(10, analysis.basic_stats(data, 1, 0)[0],analysis.basic_stats(data, 1, 0)[3])) -print(analysis.histo_analysis(data[0])) -print(analysis.histo_analysis_2(data[0], 0.01, -1, 1)) -print(analysis.stdev_z_split(3.3, 0.2, 0.1, -5, 5)) - -x = analysis.objectives(["switch", "scale", "climb"], [0,1,2], [[0,0],[1,1],[2,0]], ["0,1", "1,1", "0,5"]) ->>>>>>> 18189b45b17228228e192c270c12c3cc2cb7f8cf diff --git a/data.txt b/data.txt new file mode 100644 index 00000000..f3a5eaeb --- /dev/null +++ b/data.txt @@ -0,0 +1,49 @@ 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