import sys path = 'J://utils' sys.path.append(path) from sklearn import cluster import common_utils as utils import clustering_utils as cl_utils import classification_utils as cutils X, _ = cl_utils.generate_synthetic_data_2d_clusters(n_samples=300, n_centers=4, cluster_std=0.60) utils.plot_data_2d(X) X, _ = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=300) utils.plot_data_2d(X) X, _ = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=300) utils.plot_data_2d(X) scoring = 's_score' agg_estimator = cluster.AgglomerativeClustering() agg_grid = { 'linkage': ['ward', 'complete', 'average'], 'n_clusters': list(range(2, 7)) } agg_final_model = cl_utils.grid_search_best_model_clustering(agg_estimator, agg_grid, X, scoring=scoring)
sys.path.append("I:/New Folder/utils") import classification_utils as cutils import clustering_utils as cl_utils from keras.layers import Dense from keras import Sequential import keras_utils as kutils from keras.utils import np_utils from sklearn import model_selection #2-d classification pattern X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1) X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000, noise=0.1) X, y = cl_utils.generate_synthetic_data_2d_clusters(n_samples=1000, n_centers=4, cluster_std=1.2) X_train, X_test, y_train, y_test = model_selection.train_test_split( X, y, test_size=0.2, random_state=1) cutils.plot_data_2d_classification(X_train, y_train) y_train1 = np_utils.to_categorical(y_train) #single layered perceptron model def getModel1(): model = Sequential() model.add(Dense(units=2, input_shape=(2, ), activation='softmax')) return model