ml-finance-python

k_nearest_neighbors.py

(1265B)

 from __future__ import print_function, division
 import numpy as np
 from mlfromscratch.utils import euclidean_distance
 
 class KNN():
     """ K Nearest Neighbors classifier.
 
     Parameters:
     -----------
     k: int
         The number of closest neighbors that will determine the class of the 
         sample that we wish to predict.
     """
     def __init__(self, k=5):
         self.k = k
 
     def _vote(self, neighbor_labels):
         """ Return the most common class among the neighbor samples """
         counts = np.bincount(neighbor_labels.astype('int'))
         return counts.argmax()
 
     def predict(self, X_test, X_train, y_train):
         y_pred = np.empty(X_test.shape[0])
         # Determine the class of each sample
         for i, test_sample in enumerate(X_test):
             # Sort the training samples by their distance to the test sample and get the K nearest
             idx = np.argsort([euclidean_distance(test_sample, x) for x in X_train])[:self.k]
             # Extract the labels of the K nearest neighboring training samples
             k_nearest_neighbors = np.array([y_train[i] for i in idx])
             # Label sample as the most common class label
             y_pred[i] = self._vote(k_nearest_neighbors)
 
         return y_pred