import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_absolute_error

perch_length = np.array([8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0, 21.0,
       21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5, 22.5, 22.7,
       23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5, 27.3, 27.5, 27.5,
       27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0, 36.5, 36.0, 37.0, 37.0,
       39.0, 39.0, 39.0, 40.0, 40.0, 40.0, 40.0, 42.0, 43.0, 43.0, 43.5,
       44.0])

perch_weight = np.array([5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0, 110.0,
       115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0, 130.0,
       150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0, 197.0,
       218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0, 514.0,
       556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0, 820.0,
       850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0, 1000.0,
       1000.0])

plt.scatter(perch_length, perch_weight)
plt.xlabel('length')
plt.ylabel('weight')

train_input, test_input, train_target, test_target = train_test_split(perch_length, perch_weight, random_state=42)
#test_array = np.array([1,2,3,4])
#test_array = test_array.reshape(2, 2)

train_input = train_input.reshape(-1, 1)
test_input = test_input.reshape(-1, 1)

knr = KNeighborsRegressor()

knr.n_neighbors = 3
knr.fit(train_input, train_target)

score_by_test_data = knr.score(test_input, test_target)


test_prediction = knr.predict(test_input)

mae = mean_absolute_error(test_target, test_prediction)

print(f'mae:{mae}')

score_by_train_data = knr.score(train_input, train_target)

print(f'score_by_test_data:{score_by_test_data}')
print(f'score_by_train_data:{score_by_train_data}')

x = np.arange(5, 45).reshape(-1, 1)
for n in [1, 5, 10]:
  knr.n_neighbors = n
  knr.fit(train_input, train_target)
  prediction = knr.predict(x)

  plt.scatter(train_input, train_target)
  plt.plot(x, prediction)
  plt.title('n_neighbors = {}'.format(n))
  plt.xlabel('length')
  plt.ylabel('weight')
  plt.show()



print('hello')