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superscript.py - v 1.
changelog: - processes data more efficiently
This commit is contained in:
ltcptgeneral
parent
aba4b44da4
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@ -423,7 +423,7 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, '
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data_t = []
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for i in range (0, len(data) - 1, 1):
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for i in range (0, len(data), 1):
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data_t.append(float(data[i]))
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_mean = mean(data_t)
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@ -559,12 +559,15 @@ def stdev_z_split(mean, stdev, delta, low_bound, high_bound): #returns n-th perc
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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 for standard deviations')
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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, 1):
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derivative.append(float(hist_data[i + 1]) - float(hist_data [i]))
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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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@ -701,7 +704,7 @@ def tanh_regression(x, y):
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def r_squared(predictions, targets): # assumes equal size inputs
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return metrics.r2_score(targets, predictions)
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return metrics.r2_score(np.array(targets), np.array(predictions))
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def rms(predictions, targets): # assumes equal size inputs
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@ -724,11 +727,11 @@ def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
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z = x_test[i]
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exec("vals.append(" + equation + ")")
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r2_test = r_squared(vals, y_test)
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rms_test = rms(vals, y_test)
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return rms_train - rms_test, r2_train - r2_test
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return r2_train - r2_test
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def strip_data(data, mode):
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@ -747,7 +750,10 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
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raise error("resolution must be int")
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x_train = x
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y_train = y
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y_train = []
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for i in range(len(y)):
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y_train.append(float(y[i]))
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x_test = []
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y_test = []
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@ -756,7 +762,7 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
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index = random.randint(0, len(x) - 1)
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x_test.append(x[index])
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y_test.append(y[index])
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y_test.append(float(y[index]))
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x_train.pop(index)
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y_train.pop(index)
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@ -769,10 +775,13 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
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r2s = []
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for i in range (0, _range + 1, 1):
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x, y, z = poly_regression(x_train, y_train, i)
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eqs.append(x)
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rmss.append(y)
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r2s.append(z)
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try:
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x, y, z = poly_regression(x_train, y_train, i)
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eqs.append(x)
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rmss.append(y)
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r2s.append(z)
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except:
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pass
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for i in range (1, 100 * resolution + 1):
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try:
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@ -792,11 +801,14 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
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except:
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pass
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x, y, z = tanh_regression(x_train, y_train)
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try:
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x, y, z = tanh_regression(x_train, y_train)
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eqs.append(x)
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rmss.append(y)
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r2s.append(z)
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eqs.append(x)
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rmss.append(y)
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r2s.append(z)
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except:
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pass
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for i in range (0, len(eqs), 1): #marks all equations where r2 = 1 as they 95% of the time overfit the data
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if r2s[i] == 1:
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@ -815,38 +827,38 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
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overfit = []
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for i in range (0, len(eqs), 1):
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overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test))
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return eqs, rmss, r2s, overfit
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def select_best_regression(eqs, rmss, r2s, overfit, selector):
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b_eq = ""
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b_rms = 0
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b_r2 = 0
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b_overfit = 0
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b_eq = ""
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b_rms = 0
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b_r2 = 0
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b_overfit = 0
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ind = 0
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ind = 0
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if selector == "min_overfit":
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if selector == "min_overfit":
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ind = np.argmax(overfit)
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ind = np.argmin(overfit)
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b_eq = eqs[ind]
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b_rms = rmss[ind]
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b_r2 = r2s[ind]
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b_overfit = overfit[ind]
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b_eq = eqs[ind]
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b_rms = rmss[ind]
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b_r2 = r2s[ind]
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b_overfit = overfit[ind]
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if selector == "max_rmss":
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if selector == "max_r2s":
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ind = np.argmax(rmss)
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ind = np.argmax(r2s)
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b_eq = eqs[ind]
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b_rms = rmss[ind]
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b_r2 = r2s[ind]
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b_overfit = overfit[ind]
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b_eq = eqs[ind]
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b_rms = rmss[ind]
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b_r2 = r2s[ind]
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b_overfit = overfit[ind]
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return b_eq, b_rms, b_r2, b_overfit
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return b_eq, b_rms, b_r2, b_overfit
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def p_value(x, y): #takes 2 1d arrays
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@ -878,7 +890,7 @@ def basic_analysis(data): #assumes that rows are the independent variable and co
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def benchmark(x, y):
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start_g = time.time()
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start_g = time.time()
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generate_data("data/data.csv", x, y, -10, 10)
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end_g = time.time()
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@ -1092,4 +1104,4 @@ def stdev(data, xbar=None):
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try:
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return var.sqrt()
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except AttributeError:
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return math.sqrt(var)
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return math.sqrt(var)
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@ -1 +1,5 @@
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2022,4,4,4,4
2011,5,5,5,5,5
1101,6,6,6,6,6,6
821374,7,7,7
5,8,8,8
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2022,4,5,10,15,8,954978,84,74
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2011,3,6,9,12,15,7,856,9
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1101,4,16,32,64,128,2,4234,-56
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821374,7,8,9,79.33333333,170.3333333,636647,5874.666667,-121
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5,4,8,8,103.8333333,230.3333333,1114135,7949.666667,-186
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5
data analysis/data/bdata.csv
Normal file
5
data analysis/data/bdata.csv
Normal file
@ -0,0 +1,5 @@
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-1.6007632575405175,3.094243071050249,4.583936305454976,7.237782504548598,-5.521589121433712,-2.9078766762969295,3.2751982610806145,3.669074500924099,6.194289395001171,-9.70395566576695,4.8593751222388235,-6.67094228013908,-4.144180498130141,-7.257062237714525,5.71197645401875,9.945491401171964,-2.7707351521125396,1.4555780053661849,2.2075220439544054,-7.918108528363314,0.6886619609074511,8.379762449827606,5.964876797994396,3.037103869702946,4.796017042338738,8.846961658102465,-8.238704985679558,4.530303869837741,-5.320585260639417,2.7702239357820417,-0.7832177468135537,9.256314936398226,-8.116813655472663,7.315710420609928,3.410242694024994,-8.10603846276264,-8.07153698072364,2.152085496135115,-3.0275379228407058,6.830181793819328,-4.29878946762698,9.088682964786567,-3.0116161280634612,2.111794120861081,1.1271566641715474,-0.35857566057631374,7.051881933450716,8.624850467592807,-9.140779075977711,-4.07202265033866,-2.246723549679377,-7.541531711756095,-2.4168165528412366,-2.7333638278084376,6.443589792007607,0.7905247877923074,0.30456961697296947,-3.990848124445174,-7.9156804373061345,9.162025498895577,-8.064463486673983,5.011434008083688,-8.958579081781028,6.271439236252764,-4.796406036363702,-3.485792580154259,-7.626666268770766,-8.897222191721939,-7.458882322500758,1.3507042532359428,-9.984387735385813,4.709713126273524,5.937388844487694,-6.1309191195400725,-5.786143582459921,8.250793995102445,7.991120247882058,-0.829775210764117,-2.892320256799506,5.713355349767738,-4.291238046532964,5.388711926791316,4.6898933351779135,8.518246578598415,5.737025842395383,-5.025250884552543,5.4223386703040095,-0.7664691196370974,-1.6417217237143138,0.15989165426631047,1.291931036048135,-1.2477263925023596,-8.385094769816812,6.680157017788698,9.200962867930386,4.438716964910279,0.05664451323347208,2.9767752824462654,9.862390452775337,-4.638188933411371
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-4.19886514948963,2.116259832903374,9.784780806656713,-6.0262515990655245,2.259850780278308,5.311171798094897,3.164512882950646,4.350057506858846,7.693618498503586,-1.0820047325626003,6.008893820630796,6.959235712776991,-2.96741010662738,-5.39553133393701,7.687926117869168,-2.214421295339248,-6.563678258550183,3.948911670515244,7.853521046343122,-6.343085246017093,2.56780495027966,-3.232660420633284,2.709342092804402,-8.913853676046354,2.628378579583048,-8.864398454413786,-6.211965077084363,8.779612488518271,1.5594177199635304,-9.772430538411676,3.7679334925591945,-2.8048191086500314,-8.586986980341946,-2.4926295457304093,-4.852547317818196,9.507065105074783,-3.566533536225176,8.196094739800053,2.2637934849874988,6.6155224065612614,-4.0133698637219,-5.911477528422056,2.6049117208119554,-1.5479147894596945,2.594762318154176,-6.7868329999599535,3.113136409844607,9.663942737830045,-7.31890030980084,-4.294092860785357,-4.288178288293631,-2.37970443013519,4.835033320384534,2.9259330858634556,-5.997674611760145,0.4356291979954996,4.836551398108565,9.782096211874464,-9.585544246376953,8.271693526960206,-2.2178050388803605,2.641436188891788,-6.023516481952649,9.792991730773505,-1.0952307943365458,6.379482453639433,7.876314648269901,3.822529107554015,8.882663756536772,5.4924638799010985,4.836648299842416,-1.6546484549283047,-3.758054369040682,-7.098680827220005,4.286604047728407,9.684572924081003,-9.275783902012751,-2.186978098830334,4.343146232468751,4.555468474798047,-4.42979277112862,5.520571646152758,-1.2990764806053807,0.08646762204405789,9.36820098083734,8.882177934124492,-9.05491849330825,7.675706379209121,0.7238793778833781,-2.8640931838596835,3.350602837405848,-6.7814627959535585,-2.0879701330108595,8.692522507298118,0.20922204583939852,-7.3232930928669475,-1.6927613308994474,3.9410921827987355,2.6057819490036422,-3.527273801049575
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6.788176993183889,6.862067286093868,5.833904296143984,-7.781034990188223,-6.349861604578797,5.599429977462325,2.015866273348969,-2.69247225804762,-9.216547862186925,1.264916962009977,0.08401595107666893,9.917929998283142,-0.3864740845943011,-5.300332868087949,0.28279740984828194,1.999495568209264,9.796631321834493,5.103888673210392,-1.0985864676389063,-1.5412686471497246,-9.055241757520072,-0.5146482007524771,7.619194187802897,-4.1503833583696315,-0.3967432906038759,-0.4864090139476289,2.0360061099349025,-1.7463749583944566,8.4807959990187,1.3567113623871876,3.568261226995567,7.436380867538176,2.7028531935229783,4.486970484574863,-1.5467004743898922,6.0296370906970616,-6.7369240379420825,2.789651300988451,4.9704006218532015,-1.075946622454973,9.022213731351385,6.103384581033261,-4.342334480326942,-1.5776770692904698,1.8555732666933125,-8.26014778013041,-2.9271472441879993,-7.810696622831273,-6.536772085042653,2.618363185220005,9.093874506934746,4.463338556116641,-1.3361678673511506,-3.874381482625207,-0.9331966743045896,2.8510099645779725,-2.9328414500285183,9.386495801036943,-1.0023751510178958,0.5229070405110505,-6.24551207787964,-6.8504950428095075,-2.581577003118321,2.759811742759439,-1.804713614337011,-9.60042177306402,-9.898981802243851,-5.808497690078598,-0.5070644534586322,-1.2974125856247802,2.4093179067071517,2.8665147335929575,6.393258617174254,7.280679378243889,1.3745005338887673,0.9585421043585711,-8.975335523683107,3.6950794022708973,-7.151413979451203,5.699888181294208,-1.0575200368490734,-6.947763211211018,-8.279391361362169,2.621379758754605,9.922894779226297,-2.9987218632524044,-6.14101642356731,-5.780200240971309,-3.3629391352116382,-0.7094394581177674,3.8115190778355146,3.663733013840126,3.0448836904165493,-4.836194399270624,-6.204235948856152,-3.5454861540508027,-3.6289943832681315,-4.926973636594846,1.929539157963685,-5.707193276216477
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|
||||
9.459443630130309,7.016111056779078,0.9921215455341255,-6.550347195636091,-1.1500161987948836,5.025520665751312,0.11820321329195416,-6.098011869080235,-7.059777773821221,9.918747974386516,0.5740936358815105,-6.074495459736933,-2.8562420467646437,2.7452132300248024,0.3961615673831034,-8.187709255602321,-2.223162885896828,2.02308233068765,2.173861910822211,7.467631143535776,-6.869303813419824,5.515660829109667,-1.1854728450344822,-7.244540221731035,-2.47741275462168,1.7005021142373273,-2.6435277014190124,2.914197127354111,-5.321090657621217,-3.405385672515453,-7.2390700266735974,-2.2543430838554563,0.5810520220327327,-3.662068584206102,-0.712093105965403,-7.033992941249638,8.947547833409896,-9.761463728561306,6.032536612477848,8.373114739282794,-9.188001492119549,-0.792249220135929,-2.286873900824819,9.12725915348836,9.847383433934262,-4.308017192739209,0.06416205096922312,8.989864884806554,-9.575146040499348,-1.4826049056824822,1.2457815252508393,-5.805141241281417,-8.534557544196815,3.76303051544658,-6.315166870161935,-0.8469444777475754,2.2715479997134445,7.940699113699367,2.0789925133343736,-4.539705528462939,-9.929346289529747,0.08885331451507206,6.586247829508064,7.617817013533614,7.1824527157653435,4.756418030566145,5.607378052322925,7.6483759904306226,-3.347173860635875,-6.528030394514559,7.727623706277711,-7.00049327736375,0.10327904242857677,7.5340647071513445,6.334205397937531,5.577405123406242,9.299609203382204,8.843550733594356,2.856351085240723,0.7394015204831224,-3.8778018128261316,3.6571621070359726,-8.185370601499404,5.933614053714207,-2.8773431938656158,8.796763464112626,-8.643277711461923,6.9056764000134265,-3.3536123917078537,-5.92664281813291,-6.513654020344379,2.1204729995170943,4.571033504057793,1.7282198881806465,-8.785991084685492,-0.5406625828468776,5.042946509631578,-6.18310119331984,-7.877843185844262,5.173734304035298
|
||||
|
@ -1 +1,5 @@
|
||||
2022,1,2,3,4
2011,5,6,7,8
1101,1000,2000,3000,4000
821374,0,0,0,0
5,3,5,6
|
||||
2022,1,2,3,4,5,6,7,8
|
||||
2011,5,6,7,8,9,10,11,12
|
||||
1101,1000,2000,3000,4000,5000,6000,7000,8000
|
||||
821374,0,0,0,0,0,0,0,1
|
||||
5,3,5,6,7,8.5,9.8,11.1,12.4
|
||||
|
||||
|
12
data analysis/keys/titanscoutandroid_firebase.json
Normal file
12
data analysis/keys/titanscoutandroid_firebase.json
Normal file
@ -0,0 +1,12 @@
|
||||
{
|
||||
"type": "service_account",
|
||||
"project_id": "titanscoutandroid",
|
||||
"private_key_id": "4fa61815e17c0b5c4dfe48445d87f8fc6d07a5d8",
|
||||
"private_key": "-----BEGIN PRIVATE KEY-----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\nSBMA/OHEodv06XqibcgovA==\n-----END PRIVATE KEY-----\n",
|
||||
"client_email": "firebase-adminsdk-wpsvx@titanscoutandroid.iam.gserviceaccount.com",
|
||||
"client_id": "114864465329268712237",
|
||||
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
|
||||
"token_uri": "https://oauth2.googleapis.com/token",
|
||||
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
|
||||
"client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-wpsvx%40titanscoutandroid.iam.gserviceaccount.com"
|
||||
}
|
||||
@ -6,6 +6,8 @@
|
||||
__version__ = "1.0.3.000"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
1.0.3.001:
|
||||
- processes data more efficiently
|
||||
1.0.3.000:
|
||||
- actually processes data
|
||||
1.0.2.000:
|
||||
@ -23,13 +25,26 @@ __author__ = (
|
||||
"Jacob Levine <jlevine@ttic.edu>,"
|
||||
)
|
||||
|
||||
import firebase_admin
|
||||
from firebase_admin import credentials
|
||||
from firebase_admin import firestore
|
||||
import analysis
|
||||
import titanlearn
|
||||
import visualization
|
||||
import os
|
||||
import glob
|
||||
import numpy as np
|
||||
|
||||
# Use a service account
|
||||
cred = credentials.Certificate('keys/titanscoutandroid_firebase.json')
|
||||
firebase_admin.initialize_app(cred)
|
||||
|
||||
db = firestore.client()
|
||||
|
||||
#get all the data
|
||||
|
||||
analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)
|
||||
|
||||
source_dir = 'data'
|
||||
file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
|
||||
data = []
|
||||
@ -50,14 +65,50 @@ teams = analysis.load_csv("data/teams.csv")
|
||||
#assumes that team number is in the first column, and that the order of teams is the same across all files
|
||||
#unhelpful comment
|
||||
for measure in data: #unpacks 3d array into 2ds
|
||||
|
||||
measure_stats = []
|
||||
|
||||
for i in range(len(measure)): #unpacks into specific teams
|
||||
|
||||
ofbest_curve = [None]
|
||||
r2best_curve = [None]
|
||||
|
||||
line = measure[i]
|
||||
line.pop(0) #removes team identifier
|
||||
measure_stats.append(teams[i] + list(analysis.basic_stats(line, 0, 0)))
|
||||
stats.append(list(measure_stats))
|
||||
|
||||
#print(line)
|
||||
|
||||
x = list(range(len(line)))
|
||||
eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
|
||||
|
||||
beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "min_overfit")
|
||||
|
||||
#print(eqs, rmss, r2s, overfit)
|
||||
|
||||
print (stats)
|
||||
# print(d)
|
||||
ofbest_curve.append(beqs)
|
||||
ofbest_curve.append(brmss)
|
||||
ofbest_curve.append(br2s)
|
||||
ofbest_curve.append(boverfit)
|
||||
ofbest_curve.pop(0)
|
||||
|
||||
#print (stats)
|
||||
#print(ofbest_curve)
|
||||
|
||||
beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")
|
||||
|
||||
r2best_curve.append(beqs)
|
||||
r2best_curve.append(brmss)
|
||||
r2best_curve.append(br2s)
|
||||
r2best_curve.append(boverfit)
|
||||
r2best_curve.pop(0)
|
||||
|
||||
#print(r2best_curve)
|
||||
|
||||
measure_stats.append(teams[i] + ["|"] + list(analysis.basic_stats(line, 0, 0)) + ["|"] + list(analysis.histo_analysis(line, 1, -3, 3)) + ["|"] + ofbest_curve + ["|"] + r2best_curve)
|
||||
|
||||
stats.append(list(measure_stats))
|
||||
|
||||
json_out = {}
|
||||
|
||||
for i in range(len(stats)):
|
||||
json_out[files[i]]=stats[i]
|
||||
|
||||
db.collection(u'stats').document(u'stats-noNN').set(json_out)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user