Commit 0bc4d53

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Algorithmica

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	`1`	`+import sys`
	`2`	`+sys.path.append("E:/New Folder/utils")`
	`3`	`+`
	`4`	`+import classification_utils as cutils`
	`5`	`+from sklearn import model_selection, naive_bayes, preprocessing`
	`6`	`+import seaborn as sns`
	`7`	`+`
	`8`	`+#2-d classification pattern`
	`9`	`+X, y = cutils.generate_linear_synthetic_data_classification(n_samples=1000, n_features=2, n_classes=2, weights=[0.5, 0.5], class_sep=2)`
	`10`	`+X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)`
	`11`	`+cutils.plot_data_2d_classification(X, y)`
	`12`	`+`
	`13`	`+X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=1)`
	`14`	`+cutils.plot_data_2d_classification(X_train, y_train)`
	`15`	`+`
	`16`	`+sns.distplot(X_train[:,0], hist=False)`
	`17`	`+sns.distplot(X_train[:,1], hist=False)`
	`18`	`+`
	`19`	`+#grid search for parameter values`
	`20`	`+gnb_estimator = naive_bayes.GaussianNB()`
	`21`	`+gnb_grid = {'priors':[None] }`
	`22`	`+final_estimator = cutils.grid_search_best_model(gnb_estimator, gnb_grid, X_train, y_train)`
	`23`	`+cutils.plot_model_2d_classification(final_estimator, X_train, y_train)`
	`24`	`+`
	`25`	`+final_estimator.predict_proba(X_test)`

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