def forward(self, inputs):
self.inputs = inputs
res = []
# get the output value of each unit
for unit in self.nodes:
val = self.activation.f(dot_product(unit.weights, inputs))
unit.val = val
res.append(val)
return res
def LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100):
"""
[Section 18.6.5]
Linear classifier with logistic regression.
"""
idx_i = dataset.inputs
idx_t = dataset.target
examples = dataset.examples
num_examples = len(examples)
# X transpose
X_col = [dataset.values[i] for i in idx_i] # vertical columns of X
# add dummy
ones = [1 for _ in range(len(examples))]
X_col = [ones] + X_col
# initialize random weights
num_weights = len(idx_i) + 1
w = random_weights(min_value=-0.5, max_value=0.5, num_weights=num_weights)
for epoch in range(epochs):
err = []
h = []
# pass over all examples
for example in examples:
x = [1] + example
y = sigmoid(dot_product(w, x))
h.append(sigmoid_derivative(y))
t = example[idx_t]
err.append(t - y)
# update weights
for i in range(len(w)):
buffer = [x * y for x, y in zip(err, h)]
w[i] = w[i] + learning_rate * (dot_product(buffer, X_col[i]) /
num_examples)
def predict(example):
x = [1] + example
return sigmoid(dot_product(w, x))
return predict
def LinearLearner(dataset, learning_rate=0.01, epochs=100):
"""
[Section 18.6.4]
Linear classifier with hard threshold.
"""
idx_i = dataset.inputs
idx_t = dataset.target
examples = dataset.examples
num_examples = len(examples)
# X transpose
X_col = [dataset.values[i] for i in idx_i] # vertical columns of X
# add dummy
ones = [1 for _ in range(len(examples))]
X_col = [ones] + X_col
# initialize random weights
num_weights = len(idx_i) + 1
w = random_weights(min_value=-0.5, max_value=0.5, num_weights=num_weights)
for epoch in range(epochs):
err = []
# pass over all examples
for example in examples:
x = [1] + example
y = dot_product(w, x)
t = example[idx_t]
err.append(t - y)
# update weights
for i in range(len(w)):
w[i] = w[i] + learning_rate * (dot_product(err, X_col[i]) /
num_examples)
def predict(example):
x = [1] + example
return dot_product(w, x)
return predict
def predict(example):
x = [1] + example
return sigmoid(dot_product(w, x))
def predict(example):
x = [1] + example
return dot_product(w, x)