def forward(self, inp: Tensor) -> Tensor:
self.input = inp
self.prev_hidden = self.hidden
a = [(dot(self.w[h], inp) + dot(self.u[h], self.hidden) + self.b[h])
for h in range(self.hidden_dim)]
self.hidden = tensor_apply(tanh, a)
return self.hidden
def loop(dataset: List[Rating],
movie_vectors,
user_vectors,
learning_rate: float = None) -> None:
with tqdm.tqdm(dataset) as t:
loss = 0.0
for i, rating in enumerate(t):
movie_vector = movie_vectors[rating.movie_id]
user_vector = user_vectors[rating.user_id]
predicted = dot(user_vector, movie_vector)
error = predicted - rating.rating
loss += error**2
if learning_rate is not None:
user_gradient = [error * m_j for m_j in movie_vector]
movie_gradient = [error * u_j for u_j in user_vector]
for j in range(EMBEDDING_DIM):
user_vector[j] -= learning_rate * user_gradient[j]
movie_vector[j] -= learning_rate * movie_gradient[j]
t.set_description(f"avg loss: {loss / (i + 1)}")
def sqerror_gradients(
network: List[List[Vector]], input_vector: Vector, target_vector: Vector
) -> List[List[Vector]]:
hidden_outputs, outputs = feed_forward(network, input_vector)
output_deltas = [
output * (1 - output) * (output - target) for output, target in zip(outputs, target_vector)
]
output_grads = [
[output_deltas[i] * hidden_output for hidden_output in hidden_outputs + [1]]
for i, output_neuron in enumerate(network[-1])
]
hidden_deltas = [
hidden_output * (1 - hidden_output) * dot(output_deltas, [n[i] for n in network[-1]])
for i, hidden_output in enumerate(hidden_outputs)
]
hidden_grads = [
[hidden_deltas[i] * input for input in input_vector + [1]]
for i, hidden_neuron in enumerate(network[0])
]
return [hidden_grads, output_grads]
def neuron_output(weights: Vector, inputs: Vector) -> float:
return sigmoid(dot(weights, inputs))
def perceptron_output(weights: Vector, bias: float, x: Vector) -> float:
calculation = dot(weights, x) + bias
return step_function(calculation)
def project(v: Vector, w: Vector) -> Vector:
projection_length = dot(v, w)
return scalar_multiply(projection_length, w)
def directional_variance_gradient(data: List[Vector], w: Vector) -> Vector:
w_dir = direction(w)
return [sum(2 * dot(v, w_dir) * v[i] for v in data) for i in range(len(w))]
def directional_variance(data: List[Vector], w: Vector) -> float:
w_dir = direction(w)
return sum(dot(v, w_dir)**2 for v in data)
def transform_vector(v: Vector, components: List[Vector]) -> Vector:
return [dot(v, w) for w in components]
def _negative_log_partial_j(x: Vector, y: float, beta: Vector, j: int) -> float:
return -(y - logistic(dot(x, beta))) * x[j]
def matrix_times_vector(m: Matrix, v: Vector) -> Vector:
nr, nc = shape(m)
n = len(v)
assert nc == n
return [dot(row, v) for row in m]
def covariance(xs: List[float], ys: List[float]) -> float:
assert len(xs) == len(ys), "xs and ys be the same size"
return dot(de_mean(xs), de_mean(ys)) / (len(xs) - 1)
def ridge_penalty(beta: Vector, alpha: float) -> float:
return alpha * dot(beta[1:], beta[1:])
def predict(x: Vector, beta: Vector) -> float:
return dot(x, beta)
def cosine_similarity(v1: Vector, v2: Vector) -> float:
return dot(v1, v2) / math.sqrt(dot(v1, v1) * dot(v2, v2))
def test_dot():
assert under_test.dot([1, 2, 3], [4, 5, 6]) == 32
def forward(self, inp: Tensor) -> Tensor:
self.inp = inp
return [
dot(inp, self.w[o]) + self.b[o] for o in range(self.output_dim)
]
def _negative_log_likelihood(x: Vector, y: float, beta: Vector) -> float:
if y == 1:
return -math.log(logistic(dot(x, beta)))
else:
return -math.log(1 - logistic(dot(x, beta)))
