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

# 이 노트북의 코드에 대한 설명은 [QuantileTransformer](https://tensorflow.blog/2018/01/14/quantiletransformer/) 글을 참고하세요.

# In[1]:


get_ipython().run_line_magic('load_ext', 'watermark')
get_ipython().run_line_magic('watermark', '-v -p sklearn,numpy,scipy')


# In[2]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.preprocessing import QuantileTransformer


# In[3]:


X, y = make_blobs(n_samples=500, centers=2, random_state=4)


# In[4]:


plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='black')
plt.show()


# In[5]:


quan = QuantileTransformer(n_quantiles=100)
quan.fit(X)
print(quan.quantiles_.shape)


# In[6]:


quan.quantiles_[:10]


# In[7]:


X_quan = quan.transform(X)
plt.scatter(X_quan[:, 0], X_quan[:, 1], c=y, edgecolors='black')
plt.show()


# In[8]:


quan = QuantileTransformer(output_distribution='normal', n_quantiles=100)
X_quan = quan.fit_transform(X)
plt.scatter(X_quan[:, 0], X_quan[:, 1], c=y, edgecolors='black')
plt.show()


# In[9]:


X_quan.mean(axis=0), X_quan.std(axis=0)


# In[10]:


from sklearn.preprocessing import StandardScaler
X_std = StandardScaler().fit_transform(X)
plt.scatter(X_std[:, 0], X_std[:, 1], c=y, edgecolors='black')
plt.show()