def initialize(self, data):
"""
:param data: data, numpy 2-D array
"""
clf = KMeans(self.K)
clf.fit(data, 10)
self.centers = clf.get_centers()
self.weights = np.ones(self.K) / self.K
self.covariances = np.array(
[1e10 * np.eye(data.shape[1]) for _ in range(self.K)])
def initialize(self, data: np.ndarray):
"""
Initializes cluster centers, weights, and covariances
:param data: data, numpy 2-D array
"""
km = KMeans(self.K)
km.fit(data)
_ = km.predict(data)
self.centers = km.get_centers()
self.weights = np.random.uniform(0, 1, (self.K, ))
self.weights = self.weights / np.sum(self.weights)
self.covariances = np.array([np.eye(data.shape[-1])] * self.K) * 10e8
def initialize(self, data):
"""
:param data: data, numpy 2-D array
"""
# TODO: Initialize cluster centers, weights, and covariances
# Hint: Use K-means
km = KMeans(self.K)
rr = km.fit(data)
self.weights = np.sum(rr, axis=0) / np.sum(rr)
self.centers = km.get_centers()
dat_cov = []
self.covariances = np.zeros([self.K, 2, 2])
for j in range(rr.shape[1]):
dat_cov = data[rr[:, j] == 1]
self.covariances[j] = np.cov(dat_cov, rowvar=False)
self.r = np.zeros([len(data), self.K])