127 lines
4.0 KiB
Python
127 lines
4.0 KiB
Python
import sys
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sys.path.insert(1, '../')
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from utils.utils import *
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from utils.dataset4learners import *
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class LinearClassifier:
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def learn(self, learning_rate, epochs):
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raise NotImplementedError
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def predict(self, x):
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raise NotImplementedError
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class PerceptionLinearLearner(LinearClassifier):
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"""
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Perception linear classifier: hard threshold
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"""
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def __init__(self, dataset, learning_rate=0.01, epochs=100):
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self.idx_i = dataset.inputs
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self.idx_t = dataset.target
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self.examples = dataset.examples
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self.num_examples = len(self.examples)
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# initialize random weights
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self.w = random_weights(min_value=-0.5, max_value=0.5, num_weights=len(self.idx_i) + 1)
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# learning loop
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self.learn(learning_rate, epochs)
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def learn(self, learning_rate, epochs):
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""" learning loop """
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def loss(example, w, idx_i, idx_t):
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""" error: difference between estimation and true value """
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raise NotImplementedError
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def update(w, learning_rate, err, X_col, num_examples):
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""" update weights """
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raise NotImplementedError
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def homogeneous(num_examples):
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""" build homogeneous coordinates """
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raise NotImplementedError
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X_col = homogeneous(self.num_examples)
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for epoch in range(epochs):
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err = []
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# pass over all examples
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for example in self.examples:
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err.append(loss(example, self.w, self.idx_i, self.idx_t))
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# update weights
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update(self.w, learning_rate, err, X_col, self.num_examples)
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def predict(self, x):
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""" make prediction """
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return int(np.dot(self.w, [1] + x))
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class LogisticLinearLeaner(LinearClassifier):
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def __init__(self, dataset, learning_rate=0.01, epochs=100):
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self.idx_i = dataset.inputs
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self.idx_t = dataset.target
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self.examples = dataset.examples
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self.num_examples = len(self.examples)
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# initialize random weights
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self.w = random_weights(min_value=-0.5, max_value=0.5, num_weights=len(self.idx_i) + 1)
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# learning loop
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self.learn(learning_rate, epochs)
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def learn(self, learning_rate, epochs):
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""" learning loop """
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def loss(example, w, idx_i, idx_t, h):
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""" error: difference between estimation and true value """
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raise NotImplementedError
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def update(w, learning_rate, err, h, X_col, num_examples):
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""" update weights """
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raise NotImplementedError
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def homogeneous(num_examples):
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""" build homogeneous coordinates """
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raise NotImplementedError
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X_col = homogeneous(self.num_examples)
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for epoch in range(epochs):
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err = []
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h = []
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# pass over all examples
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for example in self.examples:
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err.append(loss(example, self.w, self.idx_i, self.idx_t, h))
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# update weights
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update(self.w, learning_rate, err, h, X_col, self.num_examples)
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def predict(self, x):
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""" make prediction """
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return int(np.dot(self.w, [1] + x))
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if __name__ == "__main__":
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iris = DataSet(name="iris")
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iris.classes_to_numbers()
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tests = [([5, 3, 1, 0.1], 0),
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([5, 3.5, 1, 0], 0),
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([6, 3, 4, 1.1], 1),
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([6, 2, 3.5, 1], 1),
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([7.5, 4, 6, 2], 2),
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([7, 3, 6, 2.5], 2)]
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print(f'===================\nperceptron:')
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perceptron = PerceptionLinearLearner(iris)
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g = grade_learner(perceptron, tests)
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print(f' learner grade: {g}')
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# assert g > 1. / 2
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e = err_ratio(perceptron, iris)
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print(f' error ration: {e}')
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# assert e < 0.4
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print(f'===================\nlogistic:')
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logisticer = LogisticLinearLeaner(iris)
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g = grade_learner(logisticer, tests)
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print(f' learner grade: {g}')
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# assert g > 1. / 2
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e = err_ratio(logisticer, iris)
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print(f' error ration: {e}')
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# assert e < 0.4
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