import numpy as np
import collections as col
import matplotlib.pyplot as plt

bream_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]
bream_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]

smelt_length = [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]
smelt_weight = [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]

fish_length = bream_length + smelt_length   # 35개 도미, 14개 빙어
fish_weight = bream_weight + smelt_weight   # 35개 도미, 14개 빙어

fish_data = np.column_stack((fish_length,fish_weight)) #49*2 행렬
fish_target = np.concatenate((np.ones(35),np.zeros(14)))


from sklearn.model_selection import train_test_split

train_input, test_input, train_target, test_target =  train_test_split(fish_data, fish_target, stratify=fish_target, random_state = 42)

mean = np.mean(train_input,axis=0)
std = np.std(train_input,axis=0)


train_scaled = (train_input-mean) / std
test_scaled = (test_input-mean) / std

from sklearn.neighbors import KNeighborsClassifier
kn = KNeighborsClassifier()

kn.fit(train_scaled, train_target)
kn.score(test_scaled,test_target)

new = ([25,150]-mean)/std
print(kn.predict([new]))

distances, indexs = kn.kneighbors([new])

plt.scatter(train_scaled[:,0],train_scaled[:,1])
plt.scatter(new[0], new[1], marker='^')
plt.scatter(train_scaled[indexs,0],train_scaled[indexs,1], marker='D')
plt.xlabel("length")
plt.ylabel("weight")
