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
