def LinearLearner(dataset, learning_rate=0.01, epochs=100):
"""Define with learner = LinearLearner(data); infer with learner(x)."""
idx_i = dataset.inputs
idx_t = dataset.target # As of now, dataset.target gives only one index.
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 weigts
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 = dotproduct(w, x)
t = example[idx_t]
err.append(t - y)
# update weights
for i in range(len(w)):
w[i] = w[i] + learning_rate * (dotproduct(err, X_col[i]) / num_examples)
def predict(example):
x = [1] + example
return dotproduct(w, x)
return predict
def __init__(self, in_size=3, out_size=3, activation=None):
super().__init__(out_size)
self.out_size = out_size
self.inputs = None
self.activation = Sigmoid() if not activation else activation
# initialize weights
for node in self.nodes:
node.weights = random_weights(-0.5, 0.5, in_size)
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 LogisticLinearLeaner(dataset, learning_rate=0.01, epochs=100):
"""Define logistic regression classifier in 18.6.5"""
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 = 1 / (1 + math.exp(-dotproduct(w, x)))
h.append(y * (1 - 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 * (dotproduct(err, X_col[i]) / num_examples)
w[i] = w[i] + learning_rate * (dotproduct(buffer, X_col[i]) /
num_examples)
def predict(example):
x = [1] + example
return 1 / (1 + math.exp(-dotproduct(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
