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

# # 훈련 세트와 테스트 세트

# <table align="left">
#   <td>
#     <a target="_blank" href="https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/2-1.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />구글 코랩에서 실행하기</a>
#   </td>
# </table>

# ## 훈련 세트와 테스트 세트

# In[1]:


fish_length = [25.4, 26.3, 26.5, 29.0, 29.0, 29.7, 29.7, 30.0, 30.0, 30.7, 31.0, 31.0,
                31.5, 32.0, 32.0, 32.0, 33.0, 33.0, 33.5, 33.5, 34.0, 34.0, 34.5, 35.0,
                35.0, 35.0, 35.0, 36.0, 36.0, 37.0, 38.5, 38.5, 39.5, 41.0, 41.0, 9.8,
                10.5, 10.6, 11.0, 11.2, 11.3, 11.8, 11.8, 12.0, 12.2, 12.4, 13.0, 14.3, 15.0]
fish_weight = [242.0, 290.0, 340.0, 363.0, 430.0, 450.0, 500.0, 390.0, 450.0, 500.0, 475.0, 500.0,
                500.0, 340.0, 600.0, 600.0, 700.0, 700.0, 610.0, 650.0, 575.0, 685.0, 620.0, 680.0,
                700.0, 725.0, 720.0, 714.0, 850.0, 1000.0, 920.0, 955.0, 925.0, 975.0, 950.0, 6.7,
                7.5, 7.0, 9.7, 9.8, 8.7, 10.0, 9.9, 9.8, 12.2, 13.4, 12.2, 19.7, 19.9]


# In[2]:


fish_data = [[l, w] for l, w in zip(fish_length, fish_weight)]
fish_target = [1]*35 + [0]*14


# In[3]:


from sklearn.neighbors import KNeighborsClassifier

kn = KNeighborsClassifier()


# In[4]:


print(fish_data[4])


# In[5]:


print(fish_data[0:5])


# In[6]:


print(fish_data[:5])


# In[7]:


print(fish_data[44:])


# In[8]:


train_input = fish_data[:35]
train_target = fish_target[:35]

test_input = fish_data[35:]
test_target = fish_target[35:]


# In[9]:


kn.fit(train_input, train_target)
kn.score(test_input, test_target)


# ## 넘파이

# In[10]:


import numpy as np


# In[11]:


input_arr = np.array(fish_data)
target_arr = np.array(fish_target)


# In[12]:


print(input_arr)


# In[13]:


print(input_arr.shape)


# In[14]:


np.random.seed(42)
index = np.arange(49)
np.random.shuffle(index)


# In[15]:


print(index)


# In[16]:


print(input_arr[[1,3]])


# In[17]:


train_input = input_arr[index[:35]]
train_target = target_arr[index[:35]]


# In[18]:


print(input_arr[13], train_input[0])


# In[19]:


test_input = input_arr[index[35:]]
test_target = target_arr[index[35:]]


# In[20]:


import matplotlib.pyplot as plt

plt.scatter(train_input[:, 0], train_input[:, 1])
plt.scatter(test_input[:, 0], test_input[:, 1])
plt.xlabel('length')
plt.ylabel('weight')
plt.show()


# ## 두 번째 머신러닝 프로그램

# In[21]:


kn.fit(train_input, train_target)


# In[22]:


kn.score(test_input, test_target)


# In[23]:


kn.predict(test_input)


# In[24]:


test_target