def apply_affinity_prop_consort(include_transformed):
(X, y) = extract.generate_labelled_data(
valid_labels=['1'],
label_type='consort',
include_transformed=include_transformed)
am = AffinityPropagation()
preds = am.fit_predict(X)
return (X, preds)
def apply_kmeans_consort(num_clusters, include_transformed):
(X, y) = extract.generate_labelled_data(
valid_labels=['1'],
label_type='consort',
include_transformed=include_transformed)
km = KMeans(n_clusters=num_clusters, random_state=RANDOM_SEED)
preds = km.fit_predict(X)
return (X, preds, km.cluster_centers_)
def main():
(X, y) = extract.generate_labelled_data()
names = extract.get_feature_names()
print(names)
y = (map(lambda x: int(x), y))
plot_hist(y, "Distribution of consort success")
plt.show()
fig, axes = plt.subplots(nrows=5, ncols=3)
plt.tight_layout()
for i in range(np.shape(X)[1]):
plt.subplot(5, 3, i + 1)
plot_hist(X[:, i], "Distribution of " + names[i])
plt.show()
def main():
include_transformed = True
(X, y) = extract.generate_labelled_data(
include_transformed=include_transformed)
pca_vis_2d(X, y)
def main():
include_transformed = True
(attrs, labels) = extract.generate_labelled_data(
include_transformed=include_transformed)
preprocess_data(attrs, labels, None)