#!/usr/bin/env python
# coding: utf-8

# In[50]:


import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_circles
from sklearn import svm
from sklearn.svm import LinearSVC

np.random.seed(0)
xx, yy = np.meshgrid(np.linspace(-1.5, 1.5, 500), np.linspace(-1.5, 1.5, 500))

X, y = make_circles(n_samples=400, factor=.3, noise=.05)
# 線形サポートベクトルマシン
linear_svc = LinearSVC(random_state=0)
# 多項式カーネルサポートベクトルマシン
poly_svc = svm.SVC(kernel='poly')
poly_svc.fit(X, y)

# plot the line, the points, and the nearest vectors to the plane
Z = poly_svc.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='darkred')
plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='palevioletred')

X, y = make_circles(n_samples=500, factor=.3, noise=.08)
y_poly = poly_svc.predict(X)
plt.scatter(X[y_poly==0,0],X[y_poly==0,1])
plt.scatter(X[y_poly==1,0],X[y_poly==1,1])

plt.legend([a.collections[0]],
           ["learnd frontier"],
           loc="upper left",)


# In[38]:


X, y = make_circles(n_samples=500, factor=.3, noise=.08)
y_rbf = rbf_svc.predict(X)
plt.scatter(X[y_rbf==0,0],X[y_rbf==0,1])
plt.scatter(X[y_rbf==1,0],X[y_rbf==1,1])


# In[46]:


# plot the line, the points, and the nearest vectors to the plane
Z = linear_svc.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='darkred')
plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='palevioletred')

plt.legend([a.collections[0]],
           ["learnd frontier"],
           loc="upper left",)

y_linear = linear_svc.predict(X)
plt.scatter(X[y_linear==0,0],X[y_linear==0,1])
plt.scatter(X[y_linear==1,0],X[y_linear==1,1])


# In[67]:


import numpy as np
import matplotlib.pyplot as plt

from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import make_circles

np.random.seed(0)

X, y = make_circles(n_samples=400, factor=.3, noise=.05)

kpca = KernelPCA(kernel="rbf", fit_inverse_transform=True, gamma=10)
X_kpca = kpca.fit_transform(X)

plt.subplot(2, 2, 1, aspect='equal')
plt.scatter(X[:,0],X[:,1])

plt.subplot(2, 2, 2, aspect='equal')
plt.scatter(X_kpca[y==1,0],X_kpca[y==1,1])
plt.scatter(X_kpca[y==0,0],X_kpca[y==0,1])


# In[88]:


import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import make_circles

from sklearn.datasets.samples_generator import make_swiss_roll

np.random.seed(0)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

X, _ = make_swiss_roll(n_samples=1500, noise=0.05)

# 元のスイスロールを描画
#ax.scatter(X[:,0],X[:,1],X[:,2])

kpca = KernelPCA(kernel="rbf", fit_inverse_transform=True, gamma=2)
X_kpca = kpca.fit_transform(X)

ax.scatter(X_kpca[:,0],X_kpca[:,1])


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