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)