knnimpl

import numpy as np
import pandas as pd
import operator

def ed(x1, x2):
    return np.sqrt(np.sum((x1-x2)**2))

def predict(K, x_train, y_train, x_test):
  predictions = []
  for i in range(len(x_test)):
    dist = np.array([ed(x_test[i], t) for t in x_train])
    dist_sorted = dist.argsort()[:K]
    nc = {}
    for idx in dist_sorted:
      if y_train[idx] in nc:
        nc[y_train[idx]] += 1
      else:
        nc[y_train[idx]] = 1
    snc = sorted(nc.items(), reverse=True)
    predictions.append(snc[0][0])
  return predictions

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import matplotlib.pylab as plt
data = load_iris()
x_train,x_test,y_train,y_test = train_test_split(data['data'],data['target'],test_size=0.1,random_state=0)
predictions=predict(5, x_train, y_train, x_test)
print(x_test.shape,y_train.shape,x_train.shape,y_test.shape)
# print(x_train, '\n\n\n',y_train[0])
pd.scatter_matrix(x_train, y_train)
# plt.scatter(x_test, y_train[0])
print(predictions)
print('Accuracy:', accuracy_score(y_test,predictions))