def fit(self,X):
n_objects = X.shape[0]
n_features = X.shape[1]
sigma = np.zeros((self.n_clusters, n_features, n_features))
w = np.tile(1.0/self.n_clusters, self.n_clusters)
centers_idx = np.random.choice(n_objects, size=self.n_clusters, replace=False)
mu = X[centers_idx, :]
for cluster in range (self.n_clusters):
sigma[cluster :, :] = np.eye(n_features)
ll = log_likelihood(X, w, mu, sigma)
for i in range(self.max_iter):
ll_new = log_likelihood(X, w, mu, sigma)
self.save_logs(compute_labels(X, mu), w, mu, sigma, ll)
if i > 0 and abs(ll_new - ll) < self.tol:
self.cluster_centers_ = mu.copy()
self.labels_ = compute_labels(X, mu)
self.covars_ = sigma.copy()
self.w_ = w.copy()
break
else:
gamma = self.estep(X,w, mu, sigma)
w, mu, sigma = self.mstep(X,gamma)
ll = ll_new
i+=1
if i == self.max_iter:
self.convergence = -1
def fit(self, X):
n_objects, n_features = X.shape
self.covars_ = np.zeros((self.n_clusters, n_features, n_features))
self.w_ = np.tile(1.0 / self.n_clusters, self.n_clusters)
centers_idx = np.random.choice(n_objects, size = self.n_clusters, replace = False)
self.cluster_centers_ = X[centers_idx, :]
for cluster in range(self.n_clusters):
self.covars_[cluster :, :] = np.eye(n_features)
self.ll = log_likelihood(X, self.w_, self.cluster_centers_, self.covars_)
for i in range(self.max_iter):
if self.logging:
self.logs['log_likelihood'].append(log_likelihood(X, self.w_, self.cluster_centers_, self.covars_))
self.logs['labels'].append(compute_labels(X, self.cluster_centers_))
self.logs['w'].append(self.w_)
self.logs['mu'].append(self.cluster_centers_)
self.logs['sigma'].append(self.covars_)
ll_new = log_likelihood(X, self.w_, self.cluster_centers_, self.covars_)
if i > 0 and abs(ll_new - self.ll) < self.tol:
break
else:
g = self.e_step(X)
self.m_step(X, g)
self.ll = ll_new
self.labels_ = compute_labels(X, self.cluster_centers_)
def fit(self, X):
n_objects = X.shape[0]
best_log_likelihood = float('-inf')
for i in range(self.n_init):
centers_idx = np.random.choice(n_objects, size=self.n_clusters, replace=False)
mu = X[centers_idx, :]
labels = compute_labels(X, mu)
ll = log_likelihood_from_labels(X, labels)
if ll > best_log_likelihood:
best_log_likelihood = ll
self.cluster_centers_ = mu.copy()
self.labels_ = labels
docvecs_fname = "dataset/processed_data/docvecs.csv"
docvecs.to_csv(docvecs_fname, index=False, header=False)
end = int(round(time.time() * 1000))
print("Process input done! - Elapsed time: %d" % (end - begin))
# load pre-processed data
begin = int(round(time.time() * 1000))
print("Loading pre-processed data ...")
train_tfidfvecs = utils.load_vecs(
"dataset/processed_data/train/tfidfvecs.csv")
test_tfidfvecs = utils.load_vecs(
"dataset/processed_data/test/tfidfvecs.csv")
train_docvecs = utils.load_vecs("dataset/processed_data/train/docvecs.csv")
test_docvecs = utils.load_vecs("dataset/processed_data/test/docvecs.csv")
train_labels = utils.compute_labels("dataset/textdata/train/data.csv")
test_labels = utils.compute_labels("dataset/textdata/test/data.csv")
train_dataset = {
'inputs': [train_tfidfvecs, train_docvecs],
'outputs': [train_labels]
}
test_dataset = {
'inputs': [test_tfidfvecs, test_docvecs],
'outputs': [test_labels]
}
end = int(round(time.time() * 1000))
print("Compute labels done! - Elapsed time: %d" % (end - begin))
if not context_only:
checkpoint_dir = "pretrained/multiview/"
else: