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4854a14e70
@ -3,29 +3,24 @@ import numpy as np
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DIRECT_LINK = 0
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DIRECT_LINK = 0
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INDIRECT_LINK = 1
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INDIRECT_LINK = 1
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def generate_x_values(mean, std, typeConnection, y):
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def generate_x_values(mean, std, typeConnection, y):
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yMean = np.mean(y)
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yMean = np.mean(y)
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values = []
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values = []
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for cur_y in y:
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for cur_y in y:
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k = np.abs(cur_y / yMean)
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raz = np.abs(mean - np.random.normal(mean, std))
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if k > 1:
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k = 2 - 1 / k
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if typeConnection == INDIRECT_LINK:
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if typeConnection == INDIRECT_LINK:
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k = 1 / k
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raz *= -1
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if std == 0:
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if cur_y > yMean:
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k = 1
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x = mean + raz
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elif cur_y < yMean:
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x = np.random.normal(mean * (k ** 3), std * k)
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x = mean - raz
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else:
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x = mean
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values.append(x)
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values.append(x)
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# if (x > gfw.mat and cur_y > yMat) or (x < gfw.mat and cur_y < yMat):
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# dd = np.append(dd, 1)
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# else:
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# dd = np.append(dd, 0)
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# x_arr = x_arr.reshape(len(x_arr), 1)
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return np.array(values)
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return np.array(values)
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def variance_analysis(data):
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def variance_analysis(data):
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return np.array([
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return np.array([
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[np.mean(col), np.std(col), np.min(col), np.max(col)] for col in data.T
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[np.mean(col), np.std(col), np.min(col), np.max(col)] for col in data.T
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