def get_neighbors(self, training, test_instance, k):
# Get the list of nearest neighbors to a test instance
distances =[]
for i in range(len(training)-1):
dist = edit_dist(test_instance, training[i][1])
distances.append((training[i], dist))
distances.sort(key=operator.itemgetter(1))
neighbors = []
for x in range(0, k):
neighbors.append(distances[x][0])
return neighbors
def get_neighbors(self, data, data_label, test_instance, k):
# Get the list of nearest neighbors to a test instance
distances = []
for i in range(len(data)):
dist = edit_dist(test_instance, data[i])
distances.append((data[i], data_label[i], dist))
distances.sort(key=operator.itemgetter(2))
neighbors = []
for x in range(0, k):
neighbors.append([distances[x][0], distances[x][1]])
return neighbors
def get_neighbors(self, training, test_instance, k):
# Get the list of nearest neighbors to a test instance
distances = []
for i in range(len(training) - 1):
dist = edit_dist(test_instance, training[i][1])
distances.append((training[i], dist))
distances.sort(key=operator.itemgetter(1))
neighbors = []
for x in range(0, k):
neighbors.append(distances[x][0])
return neighbors
def get_neighbors(self, data, data_label, test_instance, k):
# Get the list of nearest neighbors to a test instance
distances = []
for i in range(len(data)):
dist = edit_dist(test_instance, data[i])
distances.append((data[i], data_label[i], dist))
distances.sort(key=operator.itemgetter(2))
neighbors = []
for x in range(0, k):
neighbors.append([distances[x][0], distances[x][1]])
return neighbors
def lev_metric(self, x, y):
i, j = int(x[0]), int(y[0]) # extract indices
# if self.data[i] == self.data[j]:
# print self.data[i], self.data[j], edit_dist(self.data[i], self.data[j])
return edit_dist(self.data[i], self.data[j])
def lev_metric(self, x, y):
i, j = int(x[0]), int(y[0]) # extract indices
# if self.data[i] == self.data[j]:
# print self.data[i], self.data[j], edit_dist(self.data[i], self.data[j])
return edit_dist(self.data[i], self.data[j])
