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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,96 @@ | ||
| import numpy as np | ||
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| from attempt_4.NeuralNetwork import NeuralNetwork | ||
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| class Angel: | ||
| def __init__(self, sides): | ||
| self.sides = sides | ||
| self.position = int(sides / 2) * sides + int(sides / 2) | ||
| self.moves = [] | ||
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| input_nodes = int(sides ** 2) | ||
| hidden_nodes = 140 | ||
| output_nodes = 1 | ||
| learning_rate = 0.1 | ||
| weight_wih = np.random.randn(hidden_nodes, int(input_nodes)) / np.sqrt(input_nodes) | ||
| weight_who = np.random.randn(output_nodes, hidden_nodes) / np.sqrt(hidden_nodes) | ||
| self.left = NeuralNetwork(input_nodes, hidden_nodes, output_nodes, weight_wih, weight_who, | ||
| learning_rate) | ||
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| weight_wih = np.random.randn(hidden_nodes, int(input_nodes)) / np.sqrt(input_nodes) | ||
| weight_who = np.random.randn(output_nodes, hidden_nodes) / np.sqrt(hidden_nodes) | ||
| self.right = NeuralNetwork(input_nodes, hidden_nodes, output_nodes, weight_wih, weight_who, | ||
| learning_rate) | ||
|
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| weight_wih = np.random.randn(hidden_nodes, int(input_nodes)) / np.sqrt(input_nodes) | ||
| weight_who = np.random.randn(output_nodes, hidden_nodes) / np.sqrt(hidden_nodes) | ||
| self.up = NeuralNetwork(input_nodes, hidden_nodes, output_nodes, weight_wih, weight_who, | ||
| learning_rate) | ||
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| weight_wih = np.random.randn(hidden_nodes, int(input_nodes)) / np.sqrt(input_nodes) | ||
| weight_who = np.random.randn(output_nodes, hidden_nodes) / np.sqrt(hidden_nodes) | ||
| self.down = NeuralNetwork(input_nodes, hidden_nodes, output_nodes, weight_wih, weight_who, | ||
| learning_rate) | ||
|
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| def get_last_move(self): | ||
| if not len(self.moves) == 0: | ||
| return self.moves[len(self.moves) - 1] | ||
|
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| def get_position(self): | ||
| return self.position | ||
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| def reset(self): | ||
| self.position = int(self.sides / 2) * self.sides + int(self.sides / 2) | ||
| self.moves = [] | ||
| self.left.reset() | ||
| self.up.reset() | ||
| self.right.reset() | ||
| self.down.reset() | ||
|
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| # only for player mode | ||
| def god_move(self, move): | ||
| if move == -self.sides: | ||
| self.moves.append(0) | ||
| if move == 1: | ||
| self.moves.append(1) | ||
| if move == self.sides: | ||
| self.moves.append(2) | ||
| if move == -1: | ||
| self.moves.append(3) | ||
| self.position += move | ||
|
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| # only for AI mode | ||
| def angel_move(self, board): | ||
| turn0 = self.up.query(board) | ||
| turn1 = self.right.query(board) | ||
| turn2 = self.down.query(board) | ||
| turn3 = self.left.query(board) | ||
| selection = np.argmax([turn0, turn1, turn2, turn3]) | ||
|
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| if np.argmax(selection) == 0: | ||
| self.up.save(board) | ||
| self.position += -self.sides | ||
| if np.argmax(selection) == 1: | ||
| self.right.save(board) | ||
| self.position += 1 | ||
| if np.argmax(selection) == 2: | ||
| self.down.save(board) | ||
| self.position += self.sides | ||
| if np.argmax(selection) == 3: | ||
| self.left.save(board) | ||
| self.position += -1 | ||
| self.moves.append(selection) | ||
|
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| # train angel | ||
| def train(self, has_won): | ||
| if has_won is "devil": | ||
| self.up.train(False) | ||
| self.left.train(False) | ||
| self.down.train(False) | ||
| self.right.train(False) | ||
| if has_won is "angel": | ||
| self.up.train(True) | ||
| self.left.train(True) | ||
| self.down.train(True) | ||
| self.right.train(True) |
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