KNN python 实现 (KNN from Scratch)

KNN算法实现

# implement knn from scratch
import numpy as np
from collections import Counter

def euclidean_distance(x1,x2):
	distance = np.sqrt(np.sum((x1-x2)**2))
	return distance

class KNN:
	def __init__(self, k=3):
		self.k = k

	def fit(self, X, y):
		self.X_train = X
		self.y_train = y

	def predict(self, X):
		predictions = [self._predict(x) for x in X]
		return predictions

	def _predict(self, x):
		# compute the distance
		distances = [euclidean_distance(x, x_train) for x_train in self.X_train]


		# get the closest k
		k_indices = np.argsort(distances)[:self.k]
		k_nearest_labels = [self.y_train[i] for i in k_indices]

		# majority vote
		most_common = Counter(k_nearest_labels).most_common()
		return most_common[0][0]

算法检验

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from KNN import KNN

cmap = ListedColormap(['#FF0000','#00FF00','#0000FF'])

iris = datasets.load_iris()
X, y = iris.data, iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)

plt.figure()
plt.scatter(X[:,2],X[:,3],c=y,cmap=cmap,edgecolor='k',s=20)
plt.show()

clf = KNN(k=5)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)

print(predictions)

acc = np.sum(predictions == y_test)/len(y_test)
print(acc)