From: Christian Heller Date: Mon, 13 May 2019 10:52:26 +0000 (+0200) Subject: Clean up perceptron code. X-Git-Url: https://plomlompom.com/repos/%7B%7Bdb.prefix%7D%7D/static/gitweb.js?a=commitdiff_plain;h=deb17bb919d47d7ebb85c586baa0714811feb24d;p=plomrogue2-experiments Clean up perceptron code. --- diff --git a/neural/simple_perceptron.py b/neural/simple_perceptron.py index 8af59f3..45689cb 100755 --- a/neural/simple_perceptron.py +++ b/neural/simple_perceptron.py @@ -1,83 +1,96 @@ -def step(x): - step = 0 # If 0.5, we need no bias for AND and OR; if 0, none for NOT. - # With learning, the bias will slowly balance any choice. - if x >= step: - return 1 - else: - return 0 +class Perceptron: -def result(inputs): - s = 0 - perceptron['inputs'] = inputs[:] - for i in range(len(inputs)): - s += inputs[i] * perceptron['weights'][i] - return step(s + perceptron['bias']) + def __init__(self, size): + self.n_inputs = size + self.weights = [0] * self.n_inputs + self.bias = 0 + + def output(self, inputs): + step = 0 # If 0.5, we need no bias for AND and OR; if 0, none for NOT. + # With learning, the bias will slowly balance any choice. + weighted_inputs_sum = 0 + for i in range(self.n_inputs): + weighted_inputs_sum += inputs[i] * self.weights[i] + if weighted_inputs_sum + self.bias >= step: + return 1 + else: + return 0 + +class TrainingUnit: + + def __init__(self, inputs, target): + self.inputs = inputs + self.target = target # identity -#training_set = [((0,), 0), -# ((1,), 1)] +#training_set = [TrainingUnit((0,), 0), +# TrainingUnit((1,), 1)] # NOT -#training_set = [((0,), 1), -# ((1,), 0)] +#training_set = [TrainingUnit((0,), 1), +# TrainingUnit((1,), 0)] # AND -#training_set = [((0,0), 0), -# ((1,0), 0), -# ((0,1), 0), -# ((1,1), 1)] +#training_set = [TrainingUnit((0,0), 0), +# TrainingUnit((1,0), 0), +# TrainingUnit((0,1), 0), +# TrainingUnit((1,1), 1)] # OR -#training_set = [((0,0), 0), -# ((1,0), 1), -# ((0,1), 1), -# ((1,1), 1)] +#training_set = [TrainingUnit((0,0), 0), +# TrainingUnit((1,0), 1), +# TrainingUnit((0,1), 1), +# TrainingUnit((1,1), 1)] # NOT (with one irrelevant column) -#training_set = [((0,0), 1), -# ((1,0), 0), -# ((0,1), 1), -# ((1,1), 0)] +#training_set = [TrainingUnit((0,0), 0), +# TrainingUnit((1,0), 1), +# TrainingUnit((0,1), 0), +# TrainingUnit((1,1), 1)] # XOR (will fail, as Minsky/Papert say) -#training_set = [((0,0), 0), -# ((1,0), 1), -# ((0,1), 1), -# ((1,1), 0)] +#training_set = [TrainingUnit((0,0), 0), +# TrainingUnit((1,0), 1), +# TrainingUnit((0,1), 1), +# TrainingUnit((1,1), 0)] # 1 if above f(x)=x line, else 0 -training_set = [((0,1), 1), - ((2,3), 1), - ((1,1), 0), - ((2,2), 0)] +training_set = [TrainingUnit((0,1), 1), + TrainingUnit((2,3), 1), + TrainingUnit((1,1), 0), + TrainingUnit((2,2), 0)] # 1 if above f(x)=x**2, else 0 (will fail: no linear separability) -#training_set = [((2,4), 0), -# ((2,5), 1), -# ((3,9), 0), -# ((3,10), 1)] +#training_set = [TrainingUnit((2,4), 0), +# TrainingUnit((2,5), 1), +# TrainingUnit((3,9), 0), +# TrainingUnit((3,10), 1)] -perceptron = {'weights': [0 for i in range(len(training_set[0][0]))], - 'bias': 0} -adaption_size = 0.1 -for i in range(100): +p = Perceptron(len(training_set[0].inputs)) +adaption_step = 0.1 +max_rounds = 100 +for i in range(max_rounds): print() go_on = False - for element in training_set: - inputs = element[0] - target = element[1] - result_ = result(inputs) - print("inputs %s target %s result %s correctness %5s weights %s bias %s" % (inputs, target, result_, target==result_, perceptron['weights'], perceptron['bias'])) - if target != result_: + for unit in training_set: + result_ = p.output(unit.inputs) + formatted_inputs = [] + for i in unit.inputs: + formatted_inputs += ['%2d' % i] + formatted_weights = [] + for w in p.weights: + formatted_weights += ['% .1f' % w] + print("inputs (%s) target %s result %s correctness %5s weights [%s] bias % .1f" % (', '.join(formatted_inputs), unit.target, result_, unit.target==result_, ', '.join(formatted_weights), p.bias)) + if unit.target != result_: go_on=True - perceptron['bias'] += adaption_size * (target - result_) - for i in range(len(perceptron['weights'])): - perceptron['weights'][i] += adaption_size * (target - result_) * perceptron['inputs'][i] + p.bias += adaption_step * (unit.target - result_) + for i in range(p.n_inputs): + p.weights[i] += adaption_step * (unit.target - result_) * unit.inputs[i] if not go_on: break print() if go_on: - print('COULD NOT SOLVE.') + print('COULD NOT SOLVE WITHIN %s ROUNDS.' % max_rounds) else: - print('SUCCESS') + print('SUCCESS.')