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analysis.py v 1.2.0.006
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@ -7,10 +7,12 @@
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# current benchmark of optimization: 1.33 times faster
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# current benchmark of optimization: 1.33 times faster
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# setup:
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# setup:
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__version__ = "1.2.0.005"
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__version__ = "1.2.0.006"
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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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1.2.0.006:
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- renamed func functions in regression to lin, log, exp, and sig
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1.2.0.005:
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1.2.0.005:
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- moved random_forrest_regressor and random_forrest_classifier to RandomForrest class
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- moved random_forrest_regressor and random_forrest_classifier to RandomForrest class
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- renamed Metrics to Metric
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- renamed Metrics to Metric
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@ -392,11 +394,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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try:
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try:
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def func(x, a, b):
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def lin(x, a, b):
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return a * x + b
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return a * x + b
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popt, pcov = scipy.optimize.curve_fit(func, X, y)
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popt, pcov = scipy.optimize.curve_fit(lin, X, y)
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regressions.append((popt.flatten().tolist(), None))
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regressions.append((popt.flatten().tolist(), None))
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@ -408,11 +410,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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try:
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try:
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def func(x, a, b, c, d):
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def log(x, a, b, c, d):
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return a * np.log(b*(x + c)) + d
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return a * np.log(b*(x + c)) + d
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popt, pcov = scipy.optimize.curve_fit(func, X, y)
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popt, pcov = scipy.optimize.curve_fit(log, X, y)
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regressions.append((popt.flatten().tolist(), None))
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regressions.append((popt.flatten().tolist(), None))
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@ -424,11 +426,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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try:
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try:
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def func(x, a, b, c, d):
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def exp(x, a, b, c, d):
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return a * np.exp(b*(x + c)) + d
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return a * np.exp(b*(x + c)) + d
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popt, pcov = scipy.optimize.curve_fit(func, X, y)
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popt, pcov = scipy.optimize.curve_fit(exp, X, y)
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regressions.append((popt.flatten().tolist(), None))
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regressions.append((popt.flatten().tolist(), None))
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@ -463,11 +465,11 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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try:
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try:
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def func(x, a, b, c, d):
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def sig(x, a, b, c, d):
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return a * np.tanh(b*(x + c)) + d
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return a * np.tanh(b*(x + c)) + d
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popt, pcov = scipy.optimize.curve_fit(func, X, y)
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popt, pcov = scipy.optimize.curve_fit(sig, X, y)
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regressions.append((popt.flatten().tolist(), None))
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regressions.append((popt.flatten().tolist(), None))
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