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- from builtins import range
- from builtins import object
- import numpy as np
- from past.builtins import xrange
-
-
- class KNearestNeighbor(object):
- """ a kNN classifier with L2 distance """
-
- def __init__(self):
- pass
-
- def train(self, X, y):
- """
- Train the classifier. For k-nearest neighbors this is just
- memorizing the training data.
-
- Inputs:
- - X: A numpy array of shape (num_train, D) containing the training data
- consisting of num_train samples each of dimension D.
- - y: A numpy array of shape (N,) containing the training labels, where
- y[i] is the label for X[i].
- """
- self.X_train = X
- self.y_train = y
-
- def predict(self, X, k=1, num_loops=0):
- """
- Predict labels for test data using this classifier.
-
- Inputs:
- - X: A numpy array of shape (num_test, D) containing test data consisting
- of num_test samples each of dimension D.
- - k: The number of nearest neighbors that vote for the predicted labels.
- - num_loops: Determines which implementation to use to compute distances
- between training points and testing points.
-
- Returns:
- - y: A numpy array of shape (num_test,) containing predicted labels for the
- test data, where y[i] is the predicted label for the test point X[i].
- """
- if num_loops == 0:
- dists = self.compute_distances_no_loops(X)
- elif num_loops == 1:
- dists = self.compute_distances_one_loop(X)
- elif num_loops == 2:
- dists = self.compute_distances_two_loops(X)
- else:
- raise ValueError('Invalid value %d for num_loops' % num_loops)
-
- return self.predict_labels(dists, k=k)
-
- def compute_distances_two_loops(self, X):
- """
- Compute the distance between each test point in X and each training point
- in self.X_train using a nested loop over both the training data and the
- test data.
-
- Inputs:
- - X: A numpy array of shape (num_test, D) containing test data.
-
- Returns:
- - dists: A numpy array of shape (num_test, num_train) where dists[i, j]
- is the Euclidean distance between the ith test point and the jth training
- point.
- """
- num_test = X.shape[0]
- num_train = self.X_train.shape[0]
- dists = np.zeros((num_test, num_train))
- for i in range(num_test):
- for j in range(num_train):
- #####################################################################
- # TODO:
- #计算第i个测试点与第j个训练点之间的l2距离，并将结果存储在dists[i，j]中。
- #你不应使用循环和np.linalg.norm()函数。
- #####################################################################
- # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
-
- pass
-
- # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
- return dists
-
- def compute_distances_one_loop(self, X):
- """
- Compute the distance between each test point in X and each training point
- in self.X_train using a single loop over the test data.
-
- Input / Output: Same as compute_distances_two_loops
- """
- num_test = X.shape[0]
- num_train = self.X_train.shape[0]
- dists = np.zeros((num_test, num_train))
- for i in range(num_test):
- #######################################################################
- # TODO:
- #计算第i个测试点与所有训练点之间的l2距离，并将结果存储在dists[i，：]中。
- #不要使用np.linalg.norm()。
- #######################################################################
- # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
-
- # 注意np.sum中要加上维度axis=1才能得出正确的结果
- # 关于axis的介绍
- # https://zhuanlan.zhihu.com/p/30960190
- # 以及np.sum的介绍
- # https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html
-
- # self.X_train (5000,3072) X[i] (1,3072) (self.X_train - X[i]) (5000,3072)
- pass
-
- # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
- return dists
-
- def compute_distances_no_loops(self, X):
- """
- Compute the distance between each test point in X and each training point
- in self.X_train using no explicit loops.
-
- Input / Output: Same as compute_distances_two_loops
- """
- num_test = X.shape[0]
- num_train = self.X_train.shape[0]
- dists = np.zeros((num_test, num_train))
- #########################################################################
- # TODO:
- #在不使用任何显式循环的情况下，计算所有测试点和所有训练点之间的l2距离，
- #并将结果存储在dists中。
- #您应该仅使用基本的数组操作来实现此功能。
- #不可以使用scipy中的函数以及函数np.linalg.norm()。
- #
- #提示：尝试使用矩阵乘法和广播总和来计算l2距离。
- #########################################################################
- # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
-
- # (x-y)^2 = x^2 + y^2 - 2xy
- # reshape是为了让两个矩阵有个维度为1，这样子便可进行广播
- pass
-
- # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
- return dists
-
- def predict_labels(self, dists, k=1):
- """
- Given a matrix of distances between test points and training points,
- predict a label for each test point.
-
- Inputs:
- - dists: A numpy array of shape (num_test, num_train) where dists[i, j]
- gives the distance betwen the ith test point and the jth training point.
-
- Returns:
- - y: A numpy array of shape (num_test,) containing predicted labels for the
- test data, where y[i] is the predicted label for the test point X[i].
- """
- num_test = dists.shape[0]
- y_pred = np.zeros(num_test)
- for i in range(num_test):
- # A list of length k storing the labels of the k nearest neighbors to
- # the ith test point.
- closest_y = []
- #########################################################################
- # TODO:
- #使用距离矩阵查找第i个测试点的k个最近邻居，
- #并使用self.y_train查找这些邻居的标签。
- #将这些标签存储在closest_y中。
- #
- #提示：查阅函数numpy.argsort。
- #########################################################################
- # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
-
- # numpy.argsort 返回排序好的数列的索引
- pass
-
- # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
- #########################################################################
- # TODO:
- #
- #现在，你已经找到了k个最近邻的标签，接着需要在closest_y中找到最可能的标签。 #将此标签存储在y_pred [i]中。如果有两个标签可能性一样的话选择索引更小的那个。
- #########################################################################
- # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
-
- y_pred[i] = np.bincount(closest_y).argmax()
-
- # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
-
- return y_pred
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