def cluster_distance(cluster1, cluster2, distance_agg=min):
"""finds the aggregate distance between elements of cluster1
and elements of cluster2"""
return distance_agg([
distance(input1, input2) for input1 in get_values(cluster1)
for input2 in get_values(cluster2)
])
def knn_classify(k, labeled_points, new_point):
"""each labeled point should be a pair (point, label)"""
# order the labeled points from nearest to farthest
by_distance = sorted(labeled_points,
key=lambda (point, _): distance(point, new_point))
# find the labels for the k closest
k_nearest_labels = [label for _, label in by_distance[:k]]
# and let them vote
return majority_vote(k_nearest_labels)
y, theta_0, alpha_0)
if __name__ == "__main__":
print "using the gradient"
v = [random.randint(-10, 10) for i in range(3)]
tolerance = 0.0000001
count = 0
while True:
#print v, sum_of_squares(v)
gradient = sum_of_squares_gradient(v) # compute the gradient at v
next_v = step(v, gradient, -0.01) # take a negative gradient step
if distance(next_v, v) < tolerance: # stop if we're converging
break
v = next_v # continue if we're not
# print v
count += 1
print(str(sum_of_squares(v)) + str(v))
print "count=" + str(count)
print "minimum v", v
print "minimum value", sum_of_squares(v)
print
print "using minimize_batch"
v = [random.randint(-10, 10) for i in range(3)]
def random_distances(dim, num_pairs):
return [distance(random_point(dim), random_point(dim))
for _ in range(num_pairs)]