def squared_clustering_errors(inputs: List[Vector], k: int) -> float:
"""finds the total squared error from the k-means clustering of inputs"""
clusterer = kMeans(k)
clusterer.train(inputs)
means = clusterer.means
assignments = [clusterer.classify(input) for input in inputs]
return sum(squared_distance(input, means[cluster])
for input, cluster in zip(inputs, assignments))
def squared_clustering_errors(inputs, k):
"""finds the total squared error from k-means clustering the inputs"""
clusterer = KMeans(k)
clusterer.train(inputs)
means = clusterer.means()
assignments = map(clusterer.classify, inputs)
return sum(squared_distance(input, means[cluster])
for input, cluster in zip(inputs, assignments))
def classify(self, input):
"""return the index of the cluster closest to the input"""
return min(range(self.k),
key=lambda i: squared_distance(input, self.means[i]))
[[random.random() for _ in range(10 + 1)] for _ in range(NUM_HIDDEN)],
# Output_layer: NUM_HIDDEN inputs -> 4 outputs
[[random.random() for _ in range(NUM_HIDDEN + 1)] for _ in range(4)]
]
from vector_operations import squared_distance
learning_rate = 1.0
with tqdm.trange(500) as t:
for epoch in t:
epoch_loss = 0.0
for x, y in zip(xs, ys):
predicted = feed_forward(network, x)[-1]
epoch_loss += squared_distance(predicted, y)
gradients = sqerror_gradients(network, x, y)
# Take gradient step for each neuron in each layer
network = [[
gradient_step(neuron, grad, -learning_rate)
for neuron, grad in zip(layer, layer_grad)
] for layer, layer_grad in zip(network, gradients)]
t.set_description(f"fizz buzz (loss: {epoch_loss})")
def argmax(xs: list) -> int:
"""Returns the index of the largest value"""
return max(range(len(xs)), key=lambda i: xs[i])
assignments = new_assignments
self.means = cluster_means(self.k, inputs, assignments)
t.set_description(f"changed: {num_changed}/{len(inputs)}")
# Example: meetups
inputs: List[List[float]] = [[-14,-5],[13,13],[20,23],[-19,-11],[-9,-16],[21,27],[-49,15],[26,13],[-46,5],[-34,-1],[11,15],[-49,0],[-22,-16],[19,28],[-12,-8],[-13,-19],[-41,8],[-11,-6],[-25,-9],[-18,-3]]
random.seed(12)
clusterer = kMeans(k = 3)
clusterer.train(inputs)
means = sorted(clusterer.means) # sort for the unit test
# Check that the means are close to what we expect
assert squared_distance(means[0], [-44, 5]) < 1
assert squared_distance(means[1], [-16, -10]) < 1
assert squared_distance(means[2], [18, 20]) < 1
random.seed(12)
clusterer = kMeans(k = 2)
clusterer.train(inputs)
means = sorted(clusterer.means)
assert len(means) == 2
assert squared_distance(means[0], [-26, -5]) < 1
assert squared_distance(means[1], [18, 20]) < 1
