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Commit 9ba6cc4

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Algorithmica
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import sys
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sys.path.append("E:/New Folder/utils")
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import classification_utils as cutils
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from sklearn import preprocessing
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import numpy as np
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X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
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X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000, noise=0.1)
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cutils.plot_data_2d_classification(X, y)
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#guassian basis transformation
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tmp = np.exp(-(X ** 2).sum(1))
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X_3d = np.c_[X, tmp]
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cutils.plot_data_3d_classification(X_3d, y, new_window=True, title="Linearly separable data in 3D with basis change")
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#polynomial basis transformation
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poly_features = preprocessing.PolynomialFeatures()
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X_poly1 = poly_features.fit_transform(X)
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poly_features = preprocessing.PolynomialFeatures(degree=3)
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X_poly2 = poly_features.fit_transform(X)
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import sys
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sys.path.append("E:/New Folder/utils")
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import classification_utils as cutils
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from sklearn import model_selection, linear_model, svm, preprocessing, pipeline
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#2-d classification pattern
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X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
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X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000, noise=0.1)
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cutils.plot_data_2d_classification(X, y)
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X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=1)
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cutils.plot_data_2d_classification(X_train, y_train)
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#perceptron algorithm
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stages = [
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('features', preprocessing.PolynomialFeatures()),
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('clf', linear_model.Perceptron(max_iter=1000))
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]
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perceptron_pipeline = pipeline.Pipeline(stages)
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perceptron_pipeline_grid = {'clf__penalty':['l1'], 'clf__alpha':[0, 0.1, 0.3, 0.5], 'features__degree':[2,3,5,10]}
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pipeline_object = cutils.grid_search_best_model(perceptron_pipeline, perceptron_pipeline_grid, X_train, y_train)
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final_estimator = pipeline_object.named_steps['clf']
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print(final_estimator.intercept_)
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print(final_estimator.coef_)
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cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
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#logistic regression algorithm
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stages = [
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('features', preprocessing.PolynomialFeatures()),
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('clf', linear_model.LogisticRegression())
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]
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lr_pipeline = pipeline.Pipeline(stages)
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lr_pipeline_grid = {'clf__penalty':['l1'], 'clf__C':[0.01, 0.1, 0.3, 0.5], 'features__degree':[2,3,5,10]}
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pipeline_object = cutils.grid_search_best_model(lr_pipeline, lr_pipeline_grid, X_train, y_train)
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final_estimator = pipeline_object.named_steps['clf']
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print(final_estimator.intercept_)
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print(final_estimator.coef_)
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cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
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#linear svm algorithm
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stages = [
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('features', preprocessing.PolynomialFeatures()),
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('clf', svm.LinearSVC())
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]
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svm_pipeline = pipeline.Pipeline(stages)
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svm_pipeline_grid = {'clf__penalty':['l2'], 'clf__C':[0.01, 0.1, 0.3, 0.5], 'features__degree':[2,3,5,10, 50, 100]}
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pipeline_object = cutils.grid_search_best_model(svm_pipeline, svm_pipeline_grid, X_train, y_train)
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final_estimator = pipeline_object.named_steps['clf']
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print(final_estimator.intercept_)
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print(final_estimator.coef_)
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cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
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import sys
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sys.path.append("E:/New Folder/utils")
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import classification_utils as cutils
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from sklearn import preprocessing, linear_model, pipeline
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X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
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X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000, noise=0.1)
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cutils.plot_data_2d_classification(X, y)
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stages = [
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('features', preprocessing.PolynomialFeatures()),
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('perceptron', linear_model.Perceptron(max_iter=1000))
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]
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perceptron_pipeline = pipeline.Pipeline(stages)
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perceptron_pipeline_grid = {'perceptron__penalty':['l1'], 'perceptron__alpha':[0, 0.1, 0.3, 0.5], 'features__degree':[2,3]}
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pipeline_object = cutils.grid_search_best_model(perceptron_pipeline, perceptron_pipeline_grid, X, y)
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final_estimator = pipeline_object.named_steps['perceptron']
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print(final_estimator.intercept_)
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print(final_estimator.coef_)
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cutils.plot_model_2d_classification(pipeline_object, X, y)

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