Revert "Refactor jsut about everything"

This reverts commit e58a60ed18bde5db28ba96910df518a61b3999b2.

3 files changed, 0 insertions, 426 deletions

diff --git a/dotsandboxes/agents/algorithms/MCTS.py b/dotsandboxes/agents/algorithms/MCTS.py
deleted file mode 100644
index 6c71ba9..0000000
--- a/dotsandboxes/agents/algorithms/MCTS.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import math
-from copy import deepcopy
-from time import perf_counter
-from random import choice
-
-from GameState import GameState
-
-# Based on https://github.com/DieterBuys/mcts-player/
-
-class GameController(object):
-    def get_next_move(self, state):
-        # when you get a new move, it is assumed that the game is not ended yet
-        assert state.get_moves()
-
-
-class MCTSNode(object):
-    """Monte Carlo Tree Node.
-    Each node encapsulates a particular game state, the moves that
-    are possible from that state and the strategic information accumulated
-    by the tree search as it progressively samples the game space.
-    """
-
-    def __init__(self, state, parent=None, move=None):
-        self.parent = parent
-        self.move = move
-        self.state = state
-
-        self.plays = 0
-        self.score = 0
-
-        self.pending_moves = state.get_moves()
-        self.children = []
-
-    def select_child_ucb(self):
-        # Note that each node's plays count is equal
-        # to the sum of its children's plays
-        def ucb(child):
-            win_ratio = child.score / child.plays \
-                        + math.sqrt(2 * math.log(self.plays) / child.plays)
-            return win_ratio
-
-        return max(self.children, key=ucb)
-
-    def expand_move(self, move):
-        self.pending_moves.remove(move)  # raises KeyError
-
-        child_state = deepcopy(self.state)
-        child_state.play_move(move)
-
-        child = MCTSNode(state=child_state, parent=self, move=move)
-        self.children.append(child)
-        return child
-
-    def get_score(self, result):
-        # return result
-        if result == 0.5:
-            return result
-
-        if self.state.player == 2:
-            if self.state.next_turn_player == result:
-                return 0.0
-            else:
-                return 1.0
-        else:
-            if self.state.next_turn_player == result:
-                return 1.0
-            else:
-                return 0.0
-
-        if self.state.next_turn_player == result:
-            return 0.0
-        else:
-            return 1.0
-
-    def __repr__(self):
-        s = 'ROOT\n' if self.parent is None else ''
-
-        children_moves = [c.move for c in self.children]
-
-        s += """Score ratio: {score} / {plays}
-Pending moves: {pending_moves}
-Children's moves: {children_moves}
-State:
-{state}\n""".format(children_moves=children_moves, **self.__dict__)
-
-        return s
-
-
-class MCTSGameController(GameController):
-    """Game controller that uses MCTS to determine the next move.
-    This is the class which implements the Monte Carlo Tree Search algorithm.
-    It builds a game tree of MCTSNodes and samples the game space until a set
-    time has elapsed.
-    """
-
-    def select(self):
-        node = self.root_node
-
-        # Descend until we find a node that has pending moves, or is terminal
-        while node.pending_moves == set() and node.children != []:
-            node = node.select_child_ucb()
-
-        return node
-
-    def expand(self, node):
-        assert node.pending_moves != set()
-
-        move = choice(tuple(node.pending_moves))
-        return node.expand_move(move)
-
-    def simulate(self, state, max_iterations=1000):
-        state = deepcopy(state)
-
-        move = state.get_random_move()
-        while move is not None:
-            state.play_move(move)
-            move = state.get_random_move()
-
-            max_iterations -= 1
-            if max_iterations <= 0:
-                return 0.5  # raise exception? (game too deep to simulate)
-
-        return state.game_result
-
-    def update(self, node, result):
-        while node is not None:
-            node.plays += 1
-            node.score += node.get_score(result)
-            node = node.parent
-
-    def get_next_move(self, state, time_allowed=1.0):
-        super(MCTSGameController, self).get_next_move(state)
-
-        # Create new tree (TODO: Preserve some state for better performance?)
-        self.root_node = MCTSNode(state)
-        iterations = 0
-
-        start_time = perf_counter()
-        while perf_counter() < start_time + time_allowed:
-            node = self.select()
-
-            if node.pending_moves != set():
-                node = self.expand(node)
-
-            result = self.simulate(node.state)
-            self.update(node, result)
-
-            iterations += 1
-
-        # Return most visited node's move
-        return max(self.root_node.children, key=lambda n: n.plays).move
diff --git a/dotsandboxes/agents/algorithms/alphaBeta.py b/dotsandboxes/agents/algorithms/alphaBeta.py
deleted file mode 100644
index 8e041fe..0000000
--- a/dotsandboxes/agents/algorithms/alphaBeta.py
+++ /dev/null
@@ -1,105 +0,0 @@
-from GameState import GameState
-class GameController(object):
-    def get_next_move(self, state):
-        # when you get a new move, it is assumed that the game is not ended yet
-        assert state.get_moves()
-
-
-def alpha_beta(node, alpha, beta):
-
-    # Based on https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning#Pseudocode
-    # node needs to support three operations: isTerminal(), value(), getChildren(), maximizingPlayer()
-
-    if node.isTerminal():
-        return node.value()
-
-    if node.maximizingPlayer():
-
-        v = float("-inf")
-        for child in node.getChildren():
-
-            v = max(v, alpha_beta(child, alpha, beta))
-            alpha = max(alpha, v)
-            if beta <= alpha:
-                break
-
-    else:
-
-        v = float("inf")
-        for child in node.getChildren():
-
-            v = min(v, alpha_beta(child, alpha, beta))
-            beta = min(beta, v)
-            if beta <= alpha:
-                break
-
-    return v
-
-
-# A class for defining algorithms used (minimax and alpha-beta pruning)
-class AlphaBeta:
-
-    def miniMax(State, Ply_num):  # Function for the minimax algorithm
-
-        for i in range(State.Current.dimY):
-            for j in range(State.Current.dimX):
-                if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
-                    State.Make(j, i, True)
-                    if Ply_num < 2:
-                        return (i, j)
-
-        Minimum_Score = 1000
-        i = 0
-
-        j = 0
-        for k, z in State.children.items():
-            Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
-            if Minimum_Score > Result:
-                Minimum_Score = Result
-                i = k[0]
-                j = k[1]
-
-        return (i, j)
-
-    # Alpha-beta pruning function for taking care of Alpha values
-    def Maximum(State, Ply_num, Alpha):
-        if Ply_num == 0:
-            return State.CurrentScore
-
-        for i in range(State.Current.dimY):
-            for j in range(State.Current.dimX):
-                if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
-                    State.Make(j, i, False)
-
-        Maximum_Score = -1000
-        i = 0
-        j = 0
-        for k, z in State.children.items():
-            Result = Algo.Minimum(z, Ply_num - 1, Maximum_Score)
-            if Maximum_Score < Result:
-                Maximum_Score = Result
-            if Result > Alpha:
-                return Result
-
-        return Maximum_Score
-
-    def Minimum(State, Ply_num, Beta):  # Alpha-beta pruning function for taking care of Beta values
-        if Ply_num == 0:
-            return State.CurrentScore
-
-        for i in range(State.Current.dimY):
-            for j in range(State.Current.dimX):
-                if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
-                    State.Make(j, i, True)
-
-        Minimum_Score = 1000
-        i = 0
-        j = 0
-        for k, z in State.children.items():
-            Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
-            if Minimum_Score > Result:
-                Minimum_Score = Result
-            if Result < Beta:
-                return Result
-
-        return Minimum_Score
diff --git a/dotsandboxes/agents/algorithms/ann.py b/dotsandboxes/agents/algorithms/ann.py
deleted file mode 100644
index 05ae647..0000000
--- a/dotsandboxes/agents/algorithms/ann.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from numpy import *
-from math import sqrt
-from copy import deepcopy
-from time import time
-
-class ANN:
-	
-	"""ANN with one hidden layer, one output and full connections in between consecutive layers.
-	Initial weights are chosen from a normal distribution.
-	Activation function is tanh."""
-	
-	INIT_SIGMA = 0.02
-	REL_STOP_MARGIN = 0.01
-	MAX_ITERATIONS = 1000000
-	ACTIVATION = tanh
-	D_ACTIVATION = lambda x: 1 - tanh(x)**2 # Derivative of tanh
-	VEC_ACTIVATION = vectorize(ACTIVATION)
-	VEC_D_ACTIVATION = vectorize(D_ACTIVATION)
-	STEP_SIZE = 0.1
-	
-	def __init__(self, input_size, hidden_size):
-		
-		#self.input_size = input_size
-		#self.hidden_size = hidden_size
-		self.hidden_weights = random.normal(0, ANN.INIT_SIGMA, (hidden_size, input_size))
-		self.output_weights = random.normal(0, ANN.INIT_SIGMA, hidden_size)
-	
-	def get_weights(self):
-		return self.hidden_weights, self.output_weights
-	
-	def predict(self, input_vector):
-		
-		# Predicts the output for this input vector
-		# input_vector will be normalized
-		
-		input_vector = input_vector/linalg.norm(input_vector)
-		return ANN.ACTIVATION(dot(self.output_weights, ANN.VEC_ACTIVATION(dot(self.hidden_weights, input_vector))))
-	
-	@staticmethod
-	def frob_norm(a, b):
-		
-		# Calculates the total Frobenius norm of both matrices A and B
-		return sqrt(linalg.norm(a)**2 + linalg.norm(b)**2)
-	
-	def train(self, examples):
-		
-		#print("Training")
-		start = time()
-		
-		# examples is a list of (input, output)-tuples
-		# input will be normalized
-		# We stop when the weights have converged within some relative margin
-		
-		for example in examples:
-			example[0] = example[0]/linalg.norm(example[0])
-		
-		iteration = 0
-		while True:
-			
-			
-			# Store old weights to check for convergence later
-			prev_hidden_weights = deepcopy(self.hidden_weights)
-			prev_output_weights = deepcopy(self.output_weights)
-			
-			for k in range(len(examples)):
-				
-				input_vector, output = examples[k]
-				
-				# Calculate outputs
-				hidden_input = dot(self.hidden_weights, input_vector)
-				hidden_output = ANN.VEC_ACTIVATION(hidden_input)
-				final_input = dot(self.output_weights, hidden_output)
-				predicted_output = ANN.ACTIVATION(final_input)
-				
-				#print("Output:", output)
-				#print("Predicted output:", predicted_output)
-				
-				# Used in calculations
-				prediction_error = output - predicted_output
-				output_derivative = ANN.D_ACTIVATION(final_input)
-				
-				# Adjust output weights and calculate requested hidden change
-				requested_hidden_change = prediction_error*output_derivative*self.output_weights
-				self.output_weights = self.output_weights + ANN.STEP_SIZE*prediction_error*hidden_output
-				
-				#print("After adjusting output weights:", ANN.ACTIVATION(dot(self.output_weights, hidden_output)))
-				
-				# Backpropagate requested hidden change to adjust hidden weights
-				self.hidden_weights = self.hidden_weights + ANN.STEP_SIZE*outer(requested_hidden_change*(ANN.VEC_D_ACTIVATION(hidden_input)), input_vector)
-				
-				#print("After adjusting hidden weights:", ANN.ACTIVATION(dot(self.output_weights, ANN.VEC_ACTIVATION(dot(self.hidden_weights, input_vector)))))
-				
-				# Check stop criteria
-				iteration += 1
-				if iteration >= ANN.MAX_ITERATIONS:
-					break
-			
-			# Check stop criteria
-			if iteration >= ANN.MAX_ITERATIONS:
-				break
-			diff = ANN.frob_norm(self.hidden_weights - prev_hidden_weights, self.output_weights - prev_output_weights)
-			base = ANN.frob_norm(self.hidden_weights, self.output_weights)
-			#if base > 0 and diff/base < ANN.REL_STOP_MARGIN:
-			#	break
-		
-		print(time() - start)
-		print("Stopped training after %s iterations."%iteration)
-
-# TESTING
-
-def print_difference(ann1, ann2):
-	
-	# Prints the differences in weights in between two ANN's with identical topology
-	
-	hidden_weights1, output_weights1 = ann1.get_weights()
-	hidden_weights2, output_weights2 = ann2.get_weights()
-	hidden_diff = hidden_weights1 - hidden_weights2
-	output_diff = output_weights1 - output_weights2
-	
-	print(hidden_diff)
-	print(output_diff)
-	print("Frobenius norms:")
-	print("Hidden weights difference:", linalg.norm(hidden_diff))
-	print("Output weights difference:", linalg.norm(output_diff))
-	print("Both:", ANN.frob_norm(hidden_diff, output_diff))
-
-def RMSE(ann, examples):
-	
-	total = 0
-	for input_vector, output in examples:
-		total += (output - ann.predict(input_vector))**2
-	return sqrt(total/len(examples))
-
-def generate_examples(amount, input_size, evaluate):
-	# evaluate is a function mapping an input vector onto a numerical value
-	examples = []
-	inputs = random.normal(0, 100, (amount, input_size))
-	for i in range(amount):
-		input_vector = inputs[i]
-		examples.append([input_vector, evaluate(input_vector)])
-	return examples
-
-def test():
-	
-	# Test the ANN by having it model another ANN with identical topology but unknown weights
-	
-	input_size = 5
-	hidden_size = 3
-	real = ANN(input_size, hidden_size)
-	model = ANN(input_size, hidden_size)
-	
-	# Generate training data
-	training_data = generate_examples(10000, input_size, real.predict)
-	validation_data = generate_examples(10000, input_size, real.predict)
-	
-	# Print initial difference, train, then print new difference
-	print("Initial difference:")
-	print_difference(real, model)
-	print("Initial RMSE (on training data):", RMSE(model, training_data))
-	print("Initial RMSE (on validation data):", RMSE(model, validation_data))
-	model.train(training_data)
-	print("After training:")
-	print_difference(real, model)
-	print("After training RMSE (on training data):", RMSE(model, training_data))
-	print("After training RMSE (on validation data):", RMSE(model, validation_data))
-
-if __name__ == "__main__":
-	test()
-
-