import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import cifar

N = 100  # number of  points per class
D = 2  # dimensionality
K = 3  # number of classes
X = np.zeros((N*K, D))  # data matrix (each row = single example)
y = np.zeros(N*K, dtype='uint8')  # class labels
for j in range(K):
    ix = range(N*j, N*(j+1))
    r = np.linspace(0.0, 1, N)  # radius
    t = np.linspace(j*4, (j+1)*4, N) + np.random.rand(N) * 0.2  # theta
    X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
    y[ix] = j

# Lets visualize the data
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.show()