import os
import sys
import tarfile
import time
import pyprind
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from scipy.spatial.distance import pdist, squareform
from scipy import exp
from scipy.linalg import eigh
from sklearn.datasets import make_moons
from sklearn.datasets import make_circles
from sklearn.decomposition import KernelPCA

X, y = make_circles(n_samples=1000, random_state=123, noise=0.1, factor=0.2)

plt.scatter(X[y == 0, 0], X[y == 0, 1], color='red', marker='^', alpha=0.5)
plt.scatter(X[y == 1, 0], X[y == 1, 1], color='blue', marker='o', alpha=0.5)

plt.tight_layout()
# plt.savefig('images/05_15.png', dpi=300)
plt.show()
