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analysis.py - v 1.0.7.002
changelog: - bug fixes
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@ -7,10 +7,12 @@
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#number of easter eggs: 2
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#number of easter eggs: 2
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#setup:
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#setup:
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__version__ = "1.0.7.001"
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__version__ = "1.0.7.002"
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#changelog should be viewed using print(analysis.__changelog__)
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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__changelog__ = """changelog:
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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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1.0.7.001:
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- bug fixes
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- bug fixes
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1.0.7.000:
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1.0.7.000:
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@ -622,7 +624,11 @@ def poly_regression(x, y, power):
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for i in range(0, len(x), 1):
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for i in range(0, len(x), 1):
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z = x[i]
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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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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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_rms = rms(vals, y)
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r2_d2 = r_squared(vals, y)
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r2_d2 = r_squared(vals, y)
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@ -634,15 +640,22 @@ def log_regression(x, y, base):
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x_fit = []
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x_fit = []
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for i in range(len(x)):
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for i in range(len(x)):
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try:
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x_fit.append(np.log(x[i]) / np.log(base)) #change of base for logs
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x_fit.append(np.log(x[i]) / np.log(base)) #change of base for logs
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except:
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pass
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reg_eq = np.polyfit(x_fit, y, 1) # y = reg_eq[0] * log(x, base) + reg_eq[1]
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reg_eq = np.polyfit(x_fit, y, 1) # y = reg_eq[0] * log(x, base) + reg_eq[1]
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eq_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + str(base) +"))+" + str(reg_eq[1])
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q_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + str(base) +"))+" + str(reg_eq[1])
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vals = []
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vals = []
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for i in range(len(x)):
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for i in range(len(x)):
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z = x[i]
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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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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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_rms = rms(vals, y)
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r2_d2 = r_squared(vals, y)
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r2_d2 = r_squared(vals, y)
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@ -654,7 +667,10 @@ def exp_regression(x, y, base):
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y_fit = []
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y_fit = []
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for i in range(len(y)):
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for i in range(len(y)):
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try:
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y_fit.append(np.log(y[i]) / np.log(base)) #change of base for logs
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y_fit.append(np.log(y[i]) / np.log(base)) #change of base for logs
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except:
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pass
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reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit)) # 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)) # y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1])
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eq_str = "(" + str(base) + "**(" + str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))"
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eq_str = "(" + str(base) + "**(" + str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))"
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@ -662,7 +678,11 @@ def exp_regression(x, y, base):
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for i in range(len(x)):
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for i in range(len(x)):
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z = x[i]
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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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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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_rms = rms(vals, y)
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r2_d2 = r_squared(vals, y)
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r2_d2 = r_squared(vals, y)
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@ -681,7 +701,11 @@ def tanh_regression(x, y):
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for i in range(len(x)):
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for i in range(len(x)):
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z = x[i]
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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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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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_rms = rms(vals, y)
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r2_d2 = r_squared(vals, y)
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r2_d2 = r_squared(vals, y)
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@ -786,8 +810,6 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
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rmss.append(y)
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rmss.append(y)
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r2s.append(z)
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r2s.append(z)
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print (eqs[::-1])
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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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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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if r2s[i] == 1:
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eqs[i] = ""
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eqs[i] = ""
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@ -914,7 +936,7 @@ def debug():
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print(log_regression([1, 2, 3, 4], [2, 4, 8, 16], 2.717))
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print(log_regression([1, 2, 3, 4], [2, 4, 8, 16], 2.717))
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print(exp_regression([1, 2, 3, 4], [2, 4, 8, 16], 2.717))
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print(exp_regression([1, 2, 3, 4], [2, 4, 8, 16], 2.717))
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x, y, z, o = optimize_regression([0, 1, 2, 3, 4], [1, 2, 4, 7, 19], 10, 100)
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x, y, z, o = optimize_regression([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 2, 4, 7, 19, 22, 30, 50, 60, 80], 10, 10)
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for i in range(0, len(x), 1):
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for i in range(0, len(x), 1):
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print(str(x[i]) + " | " + str(y[i]) + " | " + str(z[i]) + " | " + str(o[i][0]) + " | " + str(o[i][1]))
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print(str(x[i]) + " | " + str(y[i]) + " | " + str(z[i]) + " | " + str(o[i][0]) + " | " + str(o[i][1]))
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