from sklearn.neural_network import MLPClassifier from sklearn.cluster import KMeans from sklearn.mixture import GaussianMixture from sklearn.model_selection import cross_val_score from data import load_tennis_data, load_titanic_data import matplotlib.pyplot as plt import numpy.random as random import numpy as np from sklearn.metrics import silhouette_samples, silhouette_score from scipy.stats import kurtosis NEGINF = -float("inf") # IMPORT DATA ten_X_train, ten_y_train, ten_X_test, ten_y_test = load_tennis_data() tit_X_train, tit_y_train, tit_X_test, tit_y_test = load_titanic_data() ten_features, ten_labels = load_tennis_data(form="df") tit_features, tit_labels = load_titanic_data(form="df") tit_cols = list(tit_features.columns) ten_cols = list(ten_features.columns) # NO DIM REDUC CLUSTERING tit_sil_em = [] ten_sil_em = [] ks = range(2, 20) for k in ks:
from sklearn.neural_network import MLPClassifier from sklearn.cluster import KMeans from sklearn.mixture import GaussianMixture from sklearn.model_selection import cross_val_score from data import load_tennis_data, load_tennis_data import matplotlib.pyplot as plt import numpy.random as random import numpy as np from sklearn.metrics import silhouette_samples, silhouette_score from scipy.stats import kurtosis NEGINF = -float("inf") # IMPORT DATA X_train, y_train, X_test, y_test = load_tennis_data() tit_X_train, tit_y_train, tit_X_test, tit_y_test = load_tennis_data() ten_df = load_tennis_data(form="original df") ten_features, ten_labels = load_tennis_data(form="df") tit_cols = list(ten_features.columns) print(tit_cols) # ICA sil_em = [] ica_models = [] em_models = [] ks = range(2, 20)