def setWeights(self, weights):
"""
Plot the linear regression line for given weights; assuming h_w(x) = weights[0]*x + weights[1].
This will draw on the existing pacman window with the existing points
weights: array or list of 2 values (or if just one value, the bias weight is assumed to be zero). If None,
no line is drawn. Default: None
"""
weights = np.array(weights)
if weights.size >= 2:
w = weights[0]
b = weights[1]
else:
w = float(weights)
b = 0
# xmin = min(x)
# xmax = max(x)
#
# ymin = w*xmin + b
# ymax = w*xmax + b
#
# point1 = (xmin+self.xShift, ymin+self.yShift)
# point2 = (xmax+self.xShift, ymax+self.yShift)
(point1, point2) = plotUtil.lineBoxIntersection(
w, -1, b, 1 - self.xShift, 1 - self.yShift,
self.width - 2 - self.xShift, self.height - 2 - self.yShift)
if point1 is not None and point2 is not None:
point1 = (point1[0] + self.xShift, point1[1] + self.yShift)
point2 = (point2[0] + self.xShift, point2[1] + self.yShift)
dx = point2[0] - point1[0]
dy = point2[1] - point1[1]
if dx == 0:
angle = 90 + 180 * dy * 1.0 / abs(dy)
else:
angle = math.atan(dy * 1.0 / dx) * 180.0 / math.pi
if self.line is not None:
graphicsUtils.remove_from_screen(self.line)
self.line = graphicsUtils.polygon(
[self.to_screen(point1),
self.to_screen(point2)],
LINE_COLOR,
filled=0,
behind=0)
if self.addPacmanToLineStart is True and len(self.agentImages) > 0:
# Bring pacman to front of display
graphicsUtils._canvas.tag_raise(self.agentImages[0][1][0])
# Put pacman at beginning of line
self.movePacman(point1, angle, self.agentImages[0][1])
graphicsUtils.refresh()
def setWeights(self, weights):
"""
Plot the logistic regression line for given weights
This will draw on the existing pacman window with the existing points
weights: array or list of 2 values (or if just one value, the bias weight is assumed to be zero). If None,
no line is drawn. Default: None
"""
weights = np.array(weights)
if weights.size >= 2:
w = weights[0]
b = weights[1]
else:
w = float(weights)
b = 0
xmin = 1 - self.xShift
xmax = self.width - 2 - self.xShift
x = np.linspace(xmin, xmax, 30)
y = 1.0 / (1 + np.exp(-(w * x + b)))
x += self.xShift
y += self.yShift
if self.line is not None:
for obj in self.line:
graphicsUtils.remove_from_screen(obj)
self.line = []
prevPoint = self.to_screen((x[0], y[0]))
for i in xrange(1, len(x)):
point = self.to_screen((x[i], y[i]))
self.line.append(graphicsUtils.line(prevPoint, point, LINE_COLOR))
prevPoint = point
# prevPoint = self.to_screen((x[0],y[0]))
# for i in xrange(1,len(x)):
# point = self.to_screen((x[i],y[i]))
# line.append(graphicsUtils.line(prevPoint, point, LINE_COLOR, filled=0, behind=0)
#
# prevPoint = point
if self.addPacmanToLineStart is True and len(self.agentImages) > 0:
# Bring pacman to front of display
graphicsUtils._canvas.tag_raise(self.agentImages[0][1][0])
# Put pacman at beginning of line
if w >= 0:
self.movePacman((x[0] - 0.5, y[0]), Directions.EAST,
self.agentImages[0][1])
else:
self.movePacman((x[-1] + 0.5, y[-1]), Directions.WEST,
self.agentImages[0][1])
graphicsUtils.refresh()
graphicsUtils.sleep(1)
def updateDistributions(self, distributions):
"Draws an agent's belief distributions"
# copy all distributions so we don't change their state
distributions = [x.copy() for x in distributions]
if self.distributionImages is None:
self.drawDistributions(self.previousState)
for x in range(len(self.distributionImages)):
for y in range(len(self.distributionImages[0])):
image = self.distributionImages[x][y]
weights = [dist[(x, y)] for dist in distributions]
if sum(weights) != 0:
pass
# Fog of war
color = [0.0, 0.0, 0.0]
colors = GHOST_VEC_COLORS[1:] # With Pacman
if self.capture:
colors = GHOST_VEC_COLORS
for weight, gcolor in zip(weights, colors):
color = [
min(1.0, c + 0.95 * g * weight**0.3)
for c, g in zip(color, gcolor)
]
changeColor(image, formatColor(*color))
refresh()
def drawWallandPositionBeliefs(self,
known_map=None,
possibleLocations=None,
direction="North",
visited_states_to_render=[],
pacman_position=None):
import random
import __main__
from graphicsUtils import draw_background, refresh
known_walls, known_non_walls = self.get_known_walls_non_walls_from_known_map(
known_map)
wallGrid = Grid(self.problem.walls.width,
self.problem.walls.height,
initialValue=False)
wallGrid.data = known_walls
allTrueWallGrid = Grid(self.problem.walls.width,
self.problem.walls.height,
initialValue=True)
# Recover list of non-wall coords:
non_wall_coords = []
for x in range(len(known_non_walls)):
for y in range(len(known_non_walls[x])):
if known_non_walls[x][y] == 1:
non_wall_coords.append((x, y))
self.display.clearExpandedCells() # Erase previous colors
self.display.drawWalls(wallGrid, formatColor(.9, 0, 0),
allTrueWallGrid)
self.display.colorCircleSquareCells(possibleLocations,
square_cells=non_wall_coords,
direction=direction,
pacman_position=pacman_position)
refresh()
def setWeights(self, weights):
"""
Plot the logistic regression line for given weights
This will draw on the existing pacman window with the existing points
weights: array or list of 2 values (or if just one value, the bias weight is assumed to be zero). If None,
no line is drawn. Default: None
"""
weights = np.array(weights)
if weights.size >= 2:
w = weights[0]
b = weights[1]
else:
w = float(weights)
b = 0
xmin = 1 - self.xShift
xmax = self.width-2 - self.xShift
x = np.linspace(xmin, xmax,30)
y = 1.0/(1+np.exp(-(w*x+b)))
x += self.xShift
y += self.yShift
if self.line is not None:
for obj in self.line:
graphicsUtils.remove_from_screen(obj)
self.line = []
prevPoint = self.to_screen((x[0],y[0]))
for i in xrange(1,len(x)):
point = self.to_screen((x[i],y[i]))
self.line.append(graphicsUtils.line(prevPoint, point, LINE_COLOR))
prevPoint = point
# prevPoint = self.to_screen((x[0],y[0]))
# for i in xrange(1,len(x)):
# point = self.to_screen((x[i],y[i]))
# line.append(graphicsUtils.line(prevPoint, point, LINE_COLOR, filled=0, behind=0)
#
# prevPoint = point
if self.addPacmanToLineStart is True and len(self.agentImages) > 0:
# Bring pacman to front of display
graphicsUtils._canvas.tag_raise(self.agentImages[0][1][0])
# Put pacman at beginning of line
if w >= 0:
self.movePacman((x[0]-0.5,y[0]), Directions.EAST, self.agentImages[0][1])
else:
self.movePacman((x[-1]+0.5,y[-1]), Directions.WEST, self.agentImages[0][1])
graphicsUtils.refresh()
def drawAgentObjects(self, state):
self.agentImages = [] # (agentState, image)
for index, agent in enumerate(state.agentStates):
if agent.isPacman:
image = self.drawPacman(agent, index)
self.agentImages.append((agent, image))
else:
image = self.drawGhost(agent, index)
self.agentImages.append((agent, image))
gU.refresh()
def setWeights(self, weights):
"""
Plot the linear regression line for given weights; assuming h_w(x) = weights[0]*x + weights[1].
This will draw on the existing pacman window with the existing points
weights: array or list of 2 values (or if just one value, the bias weight is assumed to be zero). If None,
no line is drawn. Default: None
"""
weights = np.array(weights)
if weights.size >= 2:
w = weights[0]
b = weights[1]
else:
w = float(weights)
b = 0
# xmin = min(x)
# xmax = max(x)
#
# ymin = w*xmin + b
# ymax = w*xmax + b
#
# point1 = (xmin+self.xShift, ymin+self.yShift)
# point2 = (xmax+self.xShift, ymax+self.yShift)
(point1, point2) = plotUtil.lineBoxIntersection(w, -1, b,
1-self.xShift, 1-self.yShift,
self.width-2-self.xShift, self.height-2-self.yShift)
if point1 is not None and point2 is not None:
point1 = (point1[0]+self.xShift, point1[1]+self.yShift)
point2 = (point2[0]+self.xShift, point2[1]+self.yShift)
dx = point2[0]-point1[0]
dy = point2[1]-point1[1]
if dx == 0:
angle = 90 + 180*dy*1.0/abs(dy)
else:
angle = math.atan(dy*1.0/dx)*180.0/math.pi
if self.line is not None:
graphicsUtils.remove_from_screen(self.line)
self.line = graphicsUtils.polygon([self.to_screen(point1), self.to_screen(point2)], LINE_COLOR, filled=0, behind=0)
if self.addPacmanToLineStart is True and len(self.agentImages) > 0:
# Bring pacman to front of display
graphicsUtils._canvas.tag_raise(self.agentImages[0][1][0])
# Put pacman at beginning of line
self.movePacman(point1, angle, self.agentImages[0][1])
graphicsUtils.refresh()
def plot(self, x, y, weights=None, title='Linear Regression'):
"""
Plot the input values x with their corresponding output values y (either true or predicted).
Also, plot the linear regression line if weights are given; assuming h_w(x) = weights[0]*x + weights[1].
This will draw on the existing pacman window (clearing it first) or create a new one if no window exists.
x: array or list of N scalar values.
y: array or list of N scalar values.
weights: array or list of 2 values (or if just one value, the bias weight is assumed to be zero). If None,
no line is drawn. Default: None
"""
if np.array(x).size == 0:
return
if isinstance(x[0], np.ndarray):
# Scrape the first element of each data point
x = [data[0] for data in x]
xmin = int(math.floor(min(x)))
ymin = int(math.floor(min(y)))
xmax = int(math.ceil(max(x)))
ymax = int(math.ceil(max(y)))
width = xmax - xmin + 3
height = ymax - ymin + 3
self.initPlot(xmin, ymin, width, height)
gameState = self.blankGameState.deepCopy()
gameState.agentStates = []
# Put pacman in bottom left
if self.addPacmanToLineStart is True:
gameState.agentStates.append(
AgentState(Configuration((1, 1), Directions.STOP), True))
# Add ghost at each point
for (px, py) in zip(x, y):
point = (px + self.xShift, py + self.yShift)
gameState.agentStates.append(
AgentState(Configuration(point, Directions.STOP), False))
# self.initialize(gameState)
graphicsUtils.clear_screen()
self.infoPane = InfoPane(gameState.layout, self.gridSize)
self.drawStaticObjects(gameState)
self.drawAgentObjects(gameState)
graphicsUtils.changeText(self.infoPane.scoreText, title)
graphicsUtils.refresh()
graphicsUtils.sleep(1)
if weights is not None:
self.setWeights(weights)
def setWeights(self, weights):
"""
Plot the logistic regression line for given weights
This will draw on the existing pacman window with the existing points
weights: array or list of 2 values (or if just one value, the bias weight is assumed to be zero). If None,
no line is drawn. Default: None
"""
weights = np.array(weights)
if self.prev_weights is not None:
if np.allclose(self.prev_weights, weights):
return False # no line update
self.prev_weights = weights.copy()
w1 = weights[0]
w2 = weights[1]
if weights.size >= 3:
b = weights[2]
else:
b = 0
# Line functions
# Line where w1*x1 + w2*x2 + b = 0
# x2 = -(w1*x1 + b)/w2 or
# x1 = -(w2*x2 + b)/w1
# Figure out where line intersections bounding box around points
if w1 == 0 and w2 == 0:
return
(point1, point2) = plotUtil.lineBoxIntersection(
w1, w2, b, 1 - self.xShift, 1 - self.yShift,
self.width - 2 - self.xShift, self.height - 2 - self.yShift)
if point1 is not None and point2 is not None:
point1 = (point1[0] + self.xShift, point1[1] + self.yShift)
point2 = (point2[0] + self.xShift, point2[1] + self.yShift)
if self.line is not None:
graphicsUtils.remove_from_screen(self.line)
self.line = graphicsUtils.polygon(
[self.to_screen(point1),
self.to_screen(point2)],
LINE_COLOR,
filled=0,
behind=0)
graphicsUtils.refresh()
return True # updated line
def plot(self, x, y, weights=None, title='Linear Regression'):
"""
Plot the input values x with their corresponding output values y (either true or predicted).
Also, plot the linear regression line if weights are given; assuming h_w(x) = weights[0]*x + weights[1].
This will draw on the existing pacman window (clearing it first) or create a new one if no window exists.
x: array or list of N scalar values.
y: array or list of N scalar values.
weights: array or list of 2 values (or if just one value, the bias weight is assumed to be zero). If None,
no line is drawn. Default: None
"""
if np.array(x).size == 0:
return
if isinstance(x[0], np.ndarray):
# Scrape the first element of each data point
x = [data[0] for data in x]
xmin = int(math.floor(min(x)))
ymin = int(math.floor(min(y)))
xmax = int(math.ceil(max(x)))
ymax = int(math.ceil(max(y)))
width = xmax-xmin+3
height = ymax-ymin+3
self.initPlot(xmin, ymin, width, height)
gameState = self.blankGameState.deepCopy()
gameState.agentStates = []
# Put pacman in bottom left
if self.addPacmanToLineStart is True:
gameState.agentStates.append( AgentState( Configuration( (1,1), Directions.STOP), True) )
# Add ghost at each point
for (px,py) in zip(x,y):
point = (px+self.xShift, py+self.yShift)
gameState.agentStates.append( AgentState( Configuration( point, Directions.STOP), False) )
# self.initialize(gameState)
graphicsUtils.clear_screen()
self.infoPane = InfoPane(gameState.layout, self.gridSize)
self.drawStaticObjects(gameState)
self.drawAgentObjects(gameState)
graphicsUtils.changeText(self.infoPane.scoreText, title)
graphicsUtils.refresh()
if weights is not None:
self.setWeights(weights)
def swapImages(self, agentIndex, newState):
"""
Changes an image from a ghost to a pacman or vis versa (for capture)
"""
prevState, prevImage = self.agentImages[agentIndex]
for item in prevImage:
gU.remove_from_screen(item)
if newState.isPacman:
image = self.drawPacman(newState, agentIndex)
self.agentImages[agentIndex] = (newState, image)
else:
image = self.drawGhost(newState, agentIndex)
self.agentImages[agentIndex] = (newState, image)
gU.refresh()
def setWeights(self, weights):
"""
Plot the logistic regression line for given weights
This will draw on the existing pacman window with the existing points
weights: array or list of 2 values (or if just one value, the bias weight is assumed to be zero). If None,
no line is drawn. Default: None
"""
weights = np.array(weights)
if self.prev_weights is not None:
if np.allclose(self.prev_weights, weights):
return False # no line update
self.prev_weights = weights.copy()
w1 = weights[0]
w2 = weights[1]
if weights.size >= 3:
b = weights[2]
else:
b = 0
# Line functions
# Line where w1*x1 + w2*x2 + b = 0
# x2 = -(w1*x1 + b)/w2 or
# x1 = -(w2*x2 + b)/w1
# Figure out where line intersections bounding box around points
if w1 == 0 and w2 == 0:
return
(point1, point2) = plotUtil.lineBoxIntersection(w1, w2, b,
1-self.xShift, 1-self.yShift,
self.width-2-self.xShift, self.height-2-self.yShift)
if point1 is not None and point2 is not None:
point1 = (point1[0]+self.xShift, point1[1]+self.yShift)
point2 = (point2[0]+self.xShift, point2[1]+self.yShift)
if self.line is not None:
graphicsUtils.remove_from_screen(self.line)
self.line = graphicsUtils.polygon([self.to_screen(point1), self.to_screen(point2)], LINE_COLOR, filled=0, behind=0)
graphicsUtils.refresh()
return True # updated line
def moveGhost(self, ghost, ghostIndex, prevGhost, ghostImageParts):
old_x, old_y = self.to_screen(self.getPosition(prevGhost))
new_x, new_y = self.to_screen(self.getPosition(ghost))
delta = new_x - old_x, new_y - old_y
for ghostImagePart in ghostImageParts:
gU.move_by(ghostImagePart, delta)
gU.refresh()
if ghost.scaredTimer > 0:
color = SCARED_COLOR
else:
color = GHOST_COLORS[ghostIndex]
gU.edit(ghostImageParts[0], ('fill', color), ('outline', color))
self.moveEyes(self.getPosition(ghost), self.getDirection(ghost),
ghostImageParts[-4:])
gU.refresh()
def initPlot(self, xmin, ymin, width, height):
if graphicsUtils._canvas is not None:
graphicsUtils.clear_screen()
# Initialize GameStateData with blank board with axes
self.width = width
self.height = height
self.xShift = -(xmin-1)
self.yShift = -(ymin-1)
self.line = None
self.zoom = min(30.0/self.width, 20.0/self.height)
self.gridSize = graphicsDisplay.DEFAULT_GRID_SIZE * self.zoom
# fullRow = ['%']*self.width
# row = ((self.width-1)/2)*[' '] + ['%'] + ((self.width-1)/2)*[' ']
# boardText = ((self.height-1)/2)*[row] + [fullRow] + ((self.height-1)/2)*[row]
numSpaces = self.width-1
numSpacesLeft = self.xShift
numSpacesRight = numSpaces-numSpacesLeft
numRows = self.height
numRowsBelow = self.yShift
numRowsAbove = numRows-1-numRowsBelow
fullRow = ['%']*self.width
if numSpacesLeft < 0:
row = [' ']*self.width
else:
row = numSpacesLeft*[' '] + ['%'] + numSpacesRight*[' ']
boardText = numRowsAbove*[row] + [fullRow] + numRowsBelow*[row]
layout = Layout(boardText)
self.blankGameState = GameStateData()
self.blankGameState.initialize(layout, 0)
self.initialize(self.blankGameState)
title = 'Pacman Plot'
graphicsUtils.changeText(self.infoPane.scoreText, title)
graphicsUtils.refresh()
def initPlot(self, xmin, ymin, width, height):
if graphicsUtils._canvas is not None:
graphicsUtils.clear_screen()
# Initialize GameStateData with blank board with axes
self.width = width
self.height = height
self.xShift = -(xmin-1)
self.yShift = -(ymin-1)
self.line = None
self.zoom = min(30.0/self.width, 20.0/self.height)
self.gridSize = graphicsDisplay.DEFAULT_GRID_SIZE * self.zoom
# fullRow = ['%']*self.width
# row = ((self.width-1)/2)*[' '] + ['%'] + ((self.width-1)/2)*[' ']
# boardText = ((self.height-1)/2)*[row] + [fullRow] + ((self.height-1)/2)*[row]
numSpaces = self.width-1
numSpacesLeft = self.xShift
numSpacesRight = numSpaces-numSpacesLeft
numRows = self.height
numRowsBelow = self.yShift
numRowsAbove = numRows-1-numRowsBelow
fullRow = ['%']*self.width
if numSpacesLeft < 0:
row = [' ']*self.width
else:
row = round(numSpacesLeft)*[' '] + ['%'] + round(numSpacesRight)*[' ']
boardText = round(numRowsAbove)*[row] + [fullRow] + round(numRowsBelow)*[row]
layout = Layout(boardText)
self.blankGameState = GameStateData()
self.blankGameState.initialize(layout, 0)
self.initialize(self.blankGameState)
title = 'Pacman Plot'
graphicsUtils.changeText(self.infoPane.scoreText, title)
graphicsUtils.refresh()
def drawExpandedCells(self, cells):
"""
Draws an overlay of expanded grid positions for search agents
"""
n = float(len(cells))
baseColor = [1.0, 0.0, 0.0]
self.clearExpandedCells()
self.expandedCells = []
for k, cell in enumerate(cells):
screenPos = self.to_screen(cell)
cellColor = gU.formatColor(*[(n - k) * c * .5 / n + .25
for c in baseColor])
block = gU.square(screenPos,
0.5 * self.gridSize,
color=cellColor,
filled=1, behind=2)
self.expandedCells.append(block)
if self.frameTime < 0:
gU.refresh()
def drawWallBeliefs(self,
known_map=None,
direction="North",
visited_states_to_render=[]):
import random
import __main__
from graphicsUtils import draw_background, refresh
known_walls, known_non_walls = self.get_known_walls_non_walls_from_known_map(
known_map)
wallGrid = Grid(self.problem.walls.width,
self.problem.walls.height,
initialValue=False)
wallGrid.data = known_walls
allTrueWallGrid = Grid(self.problem.walls.width,
self.problem.walls.height,
initialValue=True)
self.display.clearExpandedCells() # Erase previous colors
self.display.drawWalls(wallGrid, formatColor(.9, 0, 0),
allTrueWallGrid)
refresh()
def animatePacman(self, pacman, prevPacman, image):
if self.frameTime < 0:
print('Press any key to step forward, "q" to play')
keys = gU.wait_for_keys()
if 'q' in keys:
self.frameTime = 0.1
if self.frameTime > 0.01 or self.frameTime < 0:
fx, fy = self.getPosition(prevPacman)
px, py = self.getPosition(pacman)
frames = 4.0
for i in range(1, int(frames) + 1):
pos = px * i / frames + fx * (frames - i) / frames, \
py * i / frames + fy * (frames - i) / frames
self.movePacman(pos, self.getDirection(pacman), image)
gU.refresh()
gU.sleep(abs(self.frameTime) / frames)
else:
self.movePacman(self.getPosition(pacman),
self.getDirection(pacman), image)
gU.refresh()
def shadeCost(self, layout, constraints, costVector, feasiblePoints, xmin,
ymin, xmax, ymax):
baseColor = [1.0, 0.0, 0.0]
costs = [self.pointCost(costVector, point) for point in feasiblePoints]
minCost = min(costs)
maxCost = max(costs)
costSpan = maxCost - minCost
allFeasiblePoints = self.getFeasibleLayoutPoints(
layout, constraints, xmin, ymin, xmax, ymax)
# The feasible points themselves may have been gridded to infeasible grid points,
# but we want to make sure they are shaded too.
#cornerPoints = [self.cartesianToLayout(xmin, ymin, xmax, ymax, point) for point in feasiblePoints]
cornerPoints = self.getLayoutPointsWithSymbol(layout, ('o', 'P'))
gridPointsToShade = cornerPoints + allFeasiblePoints
for gridPoint in gridPointsToShade:
point = self.layoutToCartesian(xmin, ymin, xmax, ymax, gridPoint)
relativeCost = (self.pointCost(costVector, point) -
minCost) * 1.0 / costSpan
# Whoops our grid points are flipped top-bottom from what to_screen expects
screenPos = self.to_screen(
(gridPoint[0], len(layout) - gridPoint[1] - 1))
cellColor = [
0.25 + 0.5 * relativeCost * channel for channel in baseColor
]
graphicsUtils.square(screenPos,
0.5 * self.gridSize,
color=graphicsUtils.formatColor(*cellColor),
filled=1,
behind=2)
graphicsUtils.refresh()
graphicsUtils.sleep(1)
def updateDistributions(self, distributions):
"Draws an agent's belief distributions"
if self.distributionImages is None:
self.drawDistributions(self.previousState)
for x in range(len(self.distributionImages)):
for y in range(len(self.distributionImages[0])):
image = self.distributionImages[x][y]
weights = [dist[(x, y)] for dist in distributions]
if sum(weights) != 0:
pass
# Fog of war
color = [0.0, 0.0, 0.0]
colors = GHOST_VEC_COLORS[1:] # With Pacman
if self.capture:
colors = GHOST_VEC_COLORS
for weight, gcolor in zip(weights, colors):
color = [min(1.0, c + 0.95 * g * weight ** .3)
for c, g in zip(color, gcolor)]
gU.changeColor(image, gU.formatColor(*color))
gU.refresh()
def plot(self, x, y, weights=None, title='Linear Classification'):
"""
Plot the 2D input points, data[i], colored based on their corresponding labels (either true or predicted).
Also, plot the linear separator line if weights are given.
This will draw on the existing pacman window (clearing it first) or create a new one if no window exists.
x: list of 2D points, where each 2D point in the list is a 2 element numpy.ndarray
y: list of N labels, one for each point in data. Labels can be of any type that can be converted
a string.
weights: array of 3 values the first two are the weight on the data and the third value is the bias
weight term. If there are only 2 values in weights, the bias term is assumed to be zero. If None,
no line is drawn. Default: None
"""
if np.array(x).size == 0:
return
# Process data, sorting by label
possibleLabels = list(set(y))
sortedX1 = {}
sortedX2 = {}
for label in possibleLabels:
sortedX1[label] = []
sortedX2[label] = []
for i in range(len(x)):
sortedX1[y[i]].append(x[i][0])
sortedX2[y[i]].append(x[i][1])
x1min = float("inf")
x1max = float("-inf")
for x1Values in sortedX1.values():
x1min = min(min(x1Values), x1min)
x1max = max(max(x1Values), x1max)
x2min = float("inf")
x2max = float("-inf")
for x2Values in sortedX2.values():
x2min = min(min(x2Values), x2min)
x2max = max(max(x2Values), x2max)
x1min = int(math.floor(x1min))
x1max = int(math.ceil(x1max))
x2min = int(math.floor(x2min))
x2max = int(math.ceil(x2max))
width = x1max - x1min + 3
height = x2max - x2min + 3
self.initPlot(x1min, x2min, width, height)
gameState = self.blankGameState.deepCopy()
gameState.agentStates = []
# Add ghost/pacman at each point
for (labelIndex, label) in enumerate(possibleLabels):
pointsX1 = sortedX1[label]
pointsX2 = sortedX2[label]
for (px, py) in zip(pointsX1, pointsX2):
point = (px + self.xShift, py + self.yShift)
agent = AgentState(Configuration(point, Directions.STOP),
False)
agent.isPacman = (labelIndex == 0)
if labelIndex == 2:
agent.scaredTimer = 1
gameState.agentStates.append(agent)
# self.initialize(gameState)
graphicsUtils.clear_screen()
self.infoPane = InfoPane(gameState.layout, self.gridSize)
self.drawStaticObjects(gameState)
self.drawAgentObjects(gameState)
graphicsUtils.changeText(self.infoPane.scoreText, title)
graphicsUtils.refresh()
if weights is not None:
self.setWeights(weights)
def plot(self, x, y, weights=None, title='Logistic Regression'):
"""
Plot the 1D input points, data[i], colored based on their corresponding labels (either true or predicted).
Also, plot the logistic function fit if weights are given.
This will draw on the existing pacman window (clearing it first) or create a new one if no window exists.
x: list of 1D points, where each 1D point in the list is a 1 element numpy.ndarray
y: list of N labels, one for each point in data. Labels can be of any type that can be converted
a string.
weights: array of 2 values the first one is the weight on the data and the second value is the bias weight term.
If there are only 1 values in weights,
the bias term is assumed to be zero. If None, no line is drawn. Default: None
"""
if np.array(x).size == 0:
return
# Process data, sorting by label
possibleLabels = list(set(y))
sortedX = {}
for label in possibleLabels:
sortedX[label] = []
for i in range(len(x)):
sortedX[y[i]].append(x[i])
xmin = int(math.floor(min(x)))
xmax = int(math.ceil(max(x)))
ymin = int(math.floor(0)) - 1
ymax = int(math.ceil(1))
width = xmax - xmin + 3
height = ymax - ymin + 3
self.initPlot(xmin, ymin, width, height)
gameState = self.blankGameState.deepCopy()
gameState.agentStates = []
# Put pacman in bottom left
if self.addPacmanToLineStart is True:
gameState.agentStates.append(
AgentState(Configuration((1, 1), Directions.STOP), True))
# Add ghost at each point
for (py, label) in enumerate(possibleLabels):
pointsX = sortedX[label]
for px in pointsX:
point = (px + self.xShift, py + self.yShift)
agent = AgentState(Configuration(point, Directions.STOP),
False)
agent.isPacman = 1 - py
gameState.agentStates.append(agent)
# self.initialize(gameState)
graphicsUtils.clear_screen()
self.infoPane = InfoPane(gameState.layout, self.gridSize)
self.drawStaticObjects(gameState)
self.drawAgentObjects(gameState)
graphicsUtils.changeText(self.infoPane.scoreText, title)
graphicsUtils.refresh()
if weights is not None:
self.setWeights(weights)
def __init__(self,
constraints=[],
infeasiblePoints=[],
feasiblePoints=[],
optimalPoint=None,
costVector=None,
zoom=1.0,
frameTime=0.0):
"""
Create and dispaly a pacman plot figure.
This will draw on the existing pacman window (clearing it first) or create a new one if no window exists.
constraints: list of inequality constraints, where each constraint w1*x + w2*y <= b is represented as a tuple ((w1, w2), b)
infeasiblePoints (food): list of points where each point is a tuple (x, y)
feasiblePoints (power): list of points where each point is a tuple (x, y)
optimalPoint (pacman): optimal point as a tuple (x, y)
costVector (shading): cost vector represented as a tuple (c1, c2), where cost is c1*x + c2*x
"""
super(PacmanPlotLP, self).__init__(zoom, frameTime)
xmin = 100000
ymin = 100000
xmax = -100000
ymax = -100000
for point in feasiblePoints:
if point[0] < xmin:
xmin = point[0]
if point[0] > xmax:
xmax = point[0]
if point[1] < ymin:
ymin = point[1]
if point[1] > ymax:
ymax = point[1]
if len(feasiblePoints) == 0:
for point in infeasiblePoints:
if point[0] < xmin:
xmin = point[0]
if point[0] > xmax:
xmax = point[0]
if point[1] < ymin:
ymin = point[1]
if point[1] > ymax:
ymax = point[1]
xmin = int(math.floor(xmin)) - 3
ymin = int(math.floor(ymin)) - 3
xmax = int(math.ceil(xmax)) + 3
ymax = int(math.ceil(ymax)) + 3
width = xmax - xmin + 1
height = ymax - ymin + 1
# p = feasiblePoints[2]
# print("p={}".format(p))
# print("feasible={}".format(self.pointFeasible(p, constraints)))
# g = self.cartesianToLayout(xmin, ymin, xmax, ymax, p)
# print("g={}".format(g))
# gr = (int(round(g[0])), int(round(g[1])))
# p2 = self.layoutToCartesian(xmin, ymin, xmax, ymax, gr)
# print("p2={}".format(p2))
# print("p2 feasible={}".format(self.pointFeasible(p2, constraints)))
layoutLists = self.blankLayoutLists(width, height)
self.addInfeasibleGhosts(layoutLists, constraints, xmin, ymin, xmax,
ymax)
layoutLists = self.changeBorderGhostsToWall(layoutLists)
for point in infeasiblePoints:
self.addCartesianPointToLayout(layoutLists, point, '.', xmin, ymin,
xmax, ymax)
for point in feasiblePoints:
self.addCartesianPointToLayout(layoutLists, point, 'o', xmin, ymin,
xmax, ymax)
if optimalPoint is not None:
self.addCartesianPointToLayout(layoutLists, optimalPoint, 'P',
xmin, ymin, xmax, ymax)
if graphicsUtils._canvas is not None:
graphicsUtils.clear_screen()
# Initialize GameStateData with blank board with axes
self.width = width
self.height = height
self.zoom = min(30.0 / self.width, 20.0 / self.height)
self.gridSize = graphicsDisplay.DEFAULT_GRID_SIZE * self.zoom
maxNumGhosts = 10000
layout = Layout(layoutLists)
self.blankGameState = GameStateData()
self.blankGameState.initialize(layout, maxNumGhosts)
self.initialize(self.blankGameState)
title = 'Pacman Plot LP'
graphicsUtils.changeText(self.infoPane.scoreText, title)
graphicsUtils.refresh()
if costVector is not None:
self.shadeCost(layoutLists, constraints, costVector,
feasiblePoints, xmin, ymin, xmax, ymax)
def movePacman(self, position, direction, image):
screenPosition = self.to_screen(position)
endpoints = self.getEndpoints(direction, position)
r = PACMAN_SCALE * self.gridSize
gU.moveCircle(image[0], screenPosition, r, endpoints)
gU.refresh()
def plot(self, x, y, weights=None, title='Logistic Regression'):
"""
Plot the 1D input points, data[i], colored based on their corresponding labels (either true or predicted).
Also, plot the logistic function fit if weights are given.
This will draw on the existing pacman window (clearing it first) or create a new one if no window exists.
x: list of 1D points, where each 1D point in the list is a 1 element numpy.ndarray
y: list of N labels, one for each point in data. Labels can be of any type that can be converted
a string.
weights: array of 2 values the first one is the weight on the data and the second value is the bias weight term.
If there are only 1 values in weights,
the bias term is assumed to be zero. If None, no line is drawn. Default: None
"""
if np.array(x).size == 0:
return
# Process data, sorting by label
possibleLabels = list(set(y))
sortedX = {}
for label in possibleLabels:
sortedX[label] = []
for i in range(len(x)):
sortedX[y[i]].append(x[i])
xmin = int(math.floor(min(x)))
xmax = int(math.ceil(max(x)))
ymin = int(math.floor(0))-1
ymax = int(math.ceil(1))
width = xmax-xmin+3
height = ymax-ymin+3
self.initPlot(xmin, ymin, width, height)
gameState = self.blankGameState.deepCopy()
gameState.agentStates = []
# Put pacman in bottom left
if self.addPacmanToLineStart is True:
gameState.agentStates.append( AgentState( Configuration( (1,1), Directions.STOP), True) )
# Add ghost at each point
for (py, label) in enumerate(possibleLabels):
pointsX = sortedX[label]
for px in pointsX:
point = (px+self.xShift, py+self.yShift)
agent = AgentState( Configuration( point, Directions.STOP), False)
agent.isPacman = 1-py
gameState.agentStates.append(agent)
# self.initialize(gameState)
graphicsUtils.clear_screen()
self.infoPane = InfoPane(gameState.layout, self.gridSize)
self.drawStaticObjects(gameState)
self.drawAgentObjects(gameState)
graphicsUtils.changeText(self.infoPane.scoreText, title)
graphicsUtils.refresh()
if weights is not None:
self.setWeights(weights)
def plot(self, x, y, weights=None, title='Linear Classification'):
"""
Plot the 2D input points, data[i], colored based on their corresponding labels (either true or predicted).
Also, plot the linear separator line if weights are given.
This will draw on the existing pacman window (clearing it first) or create a new one if no window exists.
x: list of 2D points, where each 2D point in the list is a 2 element numpy.ndarray
y: list of N labels, one for each point in data. Labels can be of any type that can be converted
a string.
weights: array of 3 values the first two are the weight on the data and the third value is the bias
weight term. If there are only 2 values in weights, the bias term is assumed to be zero. If None,
no line is drawn. Default: None
"""
if np.array(x).size == 0:
return
# Process data, sorting by label
possibleLabels = list(set(y))
sortedX1 = {}
sortedX2 = {}
for label in possibleLabels:
sortedX1[label] = []
sortedX2[label] = []
for i in range(len(x)):
sortedX1[y[i]].append(x[i][0])
sortedX2[y[i]].append(x[i][1])
x1min = float("inf")
x1max = float("-inf")
for x1Values in sortedX1.values():
x1min = min(min(x1Values), x1min)
x1max = max(max(x1Values), x1max)
x2min = float("inf")
x2max = float("-inf")
for x2Values in sortedX2.values():
x2min = min(min(x2Values), x2min)
x2max = max(max(x2Values), x2max)
x1min = int(math.floor(x1min))
x1max = int(math.ceil(x1max))
x2min = int(math.floor(x2min))
x2max = int(math.ceil(x2max))
width = x1max-x1min+3
height = x2max-x2min+3
self.initPlot(x1min, x2min, width, height)
gameState = self.blankGameState.deepCopy()
gameState.agentStates = []
# Add ghost/pacman at each point
for (labelIndex, label) in enumerate(possibleLabels):
pointsX1 = sortedX1[label]
pointsX2 = sortedX2[label]
for (px, py) in zip(pointsX1, pointsX2):
point = (px+self.xShift, py+self.yShift)
agent = AgentState( Configuration( point, Directions.STOP), False)
agent.isPacman = (labelIndex==0)
if labelIndex==2:
agent.scaredTimer = 1
gameState.agentStates.append(agent)
# self.initialize(gameState)
graphicsUtils.clear_screen()
self.infoPane = InfoPane(gameState.layout, self.gridSize)
self.drawStaticObjects(gameState)
self.drawAgentObjects(gameState)
graphicsUtils.changeText(self.infoPane.scoreText, title)
graphicsUtils.refresh()
if weights is not None:
self.setWeights(weights)
def drawStaticObjects(self, state):
layout = self.layout
self.drawWalls(layout.walls)
self.food = self.drawFood(layout.food)
self.capsules = self.drawCapsules(layout.capsules)
gU.refresh()