def _initialize_params(self, data):
km = KMeans(self.k)
km.fit(data)
self.dim = data.shape[-1]
_, self.means = km.predict(data)
self.means = np.unique(self.means, axis=0)
self.pis = np.random.uniform(0, 1, (self.k, ))
self.pis = self.pis / np.sum(self.pis)
self.covariances = np.array([np.eye(self.dim)] * self.k) * 100000000
self.gammas = np.zeros((data.shape[0], self.k))
import numpy as np from sklearn.datasets import make_blobs import matplotlib.pyplot as plt from k_means import KMeans # x,y = make_blobs(centers=3, n_samples=100,n_features=2,shuffle=True,random_state=42) # x,y = make_blobs(centers=3, n_samples=100,n_features=2) x = [[1, 1], [2, 1], [4, 3], [5, 4], [1, 3], [4, 3], [9, 8]] y = np.array(x) print(y.shape) clusters = 2 # clusters = len(np.unique(y)) # print(clusters) k = KMeans(K=clusters, iters=150, plot_steps=True) # y_pred = k.predict(x) y_pred = k.predict(y) # k.plot() # print(x)
