msaf/algorithms/fmc2d/xmeans.py
def generate_2d_data(self, N=100, K=5):
        """Generates N*K 2D data points with K means and N data points
            for each mean."""
        # Seed the random
        np.random.seed(seed=int(time.time()))

        # Amount of spread of the centroids
        spread = 30

        # Generate random data
        X = np.empty((0, 2))
        for i in range(K):
            mean = np.array([np.random.random()*spread,
                             np.random.random()*spread])
            x = np.random.normal(0.0, scale=1.0, size=(N, 2)) + mean
            X = np.append(X, x, axis=0)

        return X
def generate_2d_data(self, N=100, K=5):
        """Generates N*K 2D data points with K means and N data points
            for each mean."""
        # Seed the random
        np.random.seed(seed=int(time.time()))

        # Amount of spread of the centroids
        spread = 30

        # Generate random data
        X = np.empty((0, 2))
        for i in range(K):
            mean = np.array([np.random.random()*spread,
                             np.random.random()*spread])
            x = np.random.normal(0.0, scale=1.0, size=(N, 2)) + mean
            X = np.append(X, x, axis=0)

        return X
def generate_2d_data ( self , N = 100 , K = 5 ) : # Seed the random np . random . seed ( seed = int ( time . time ( ) ) ) # Amount of spread of the centroids spread = 30 # Generate random data X = np . empty ( ( 0 , 2 ) ) for i in range ( K ) : mean = np . array ( [ np . random . random ( ) * spread , np . random . random ( ) * spread ] ) x = np . random . normal ( 0.0 , scale = 1.0 , size = ( N , 2 ) ) + mean X = np . append ( X , x , axis = 0 ) return X
Generates N*K 2D data points with K means and N data points
            for each mean.
Generates N * K 2D data points with K means and N data points for each mean .