def _get_neighbors(self, sample_i):
neighbors = []
for _sample_i, _sample in enumerate(self.X):
if _sample_i != sample_i and euclidean_distance(
self.X[sample_i], _sample) < self.eps:
neighbors.append(_sample_i)
return np.array(neighbors)
def _closest_centroid(self, sample, centroids):
closest_i = None
closest_distance = float("inf")
for i, centroid in enumerate(centroids):
distance = euclidean_distance(sample, centroid)
if distance < closest_distance:
closest_i = i
closest_distance = distance
return closest_i
def _calculate_cost(self, X, clusters, medoids):
cost = 0
# For each cluster
for i, cluster in enumerate(clusters):
medoid = medoids[i]
for sample_i in cluster:
# Add distance between sample and medoid as cost
cost += euclidean_distance(X[sample_i], medoid)
return cost
def _closest_centroid(self, sample, centroids):
closest_i = None
closest_distance = float("inf")
for i, centroid in enumerate(centroids):
distance = euclidean_distance(sample, centroid)
if distance < closest_distance:
closest_i = i
closest_distance = distance
return closest_i
def _calculate_cost(self, X, clusters, medoids): cost = 0 # For each cluster for i, cluster in enumerate(clusters): medoid = medoids[i] for sample_i in cluster: # Add distance between sample and medoid as cost cost += euclidean_distance(X[sample_i], medoid) return cost
def predict(self, X_test, X_train, y_train): classes = np.unique(y_train) y_pred = [] for i in range(len(X_test)): test_sample = X_test[i] neighbors = [] for j in range(len(X_train)): observed_sample = X_train[j] distance = euclidean_distance(test_sample, observed_sample) label = y_train[j] neighbors.append([distance, label]) neighbors = np.array(neighbors) k_nearest_neighbors = neighbors[neighbors[:,0].argsort()][:self.k] label = self._get_vote(k_nearest_neighbors, classes) y_pred.append(label) return np.array(y_pred)
def predict(self, X_test, X_train, y_train):
classes = np.unique(y_train)
pred_classes = []
for test_sample in X_test:
# 获得[欧拉距离，标签]的矩阵
distances = []
for i, train_sample in enumerate(X_train):
distance = euclidean_distance(test_sample, train_sample)
label = y_train[i]
distances.append([distance, label])
distances = np.array(distances)
# 获得前k个最小的欧拉距离的点。
index_argsort = np.argsort(distances[:, 0])
neighbors = distances[index_argsort][:self.k]
# 利用多数表决进行投票
pred_cla = self._majority_vote(neighbors, classes)
pred_classes.append(pred_cla)
return np.array(pred_classes)
def predict(self, X_test, X_train, y_train):
classes = np.unique(y_train)
y_pred = []
# Determine the class of each sample
for test_sample in X_test:
neighbors = []
# Calculate the distance form each observed sample to the
# sample we wish to predict
for j, observed_sample in enumerate(X_train):
distance = euclidean_distance(test_sample, observed_sample)
label = y_train[j]
# Add neighbor information
neighbors.append([distance, label])
neighbors = np.array(neighbors)
# Sort the list of observed samples from lowest to highest distance
# and select the k first
k_nearest_neighbors = neighbors[neighbors[:, 0].argsort()][:self.k]
# Do a majority vote among the k neighbors and set prediction as the
# class receing the most votes
label = self._get_vote(k_nearest_neighbors, classes)
y_pred.append(label)
return np.array(y_pred)
def predict(self, X_test, X_train, y_train):
classes = np.unique(y_train)
y_pred = []
# Determine the class of each sample
for test_sample in X_test:
neighbors = []
# Calculate the distance form each observed sample to the
# sample we wish to predict
for j, observed_sample in enumerate(X_train):
distance = euclidean_distance(test_sample, observed_sample)
label = y_train[j]
# Add neighbor information
neighbors.append([distance, label])
neighbors = np.array(neighbors)
# Sort the list of observed samples from lowest to highest distance
# and select the k first
k_nearest_neighbors = neighbors[neighbors[:, 0].argsort()][:self.k]
# Do a majority vote among the k neighbors and set prediction as the
# class receing the most votes
label = self._majority_vote(k_nearest_neighbors, classes)
y_pred.append(label)
return np.array(y_pred)
