def reset_utils(self):
self.utility = resize(0.0, (self.num_squares_x, self.num_squares_y))
for e in self.pits:
self.utility[e] = -1.0
e = self.goal
self.utility[e] = 1.0
def corrcoef(*args):
"""
corrcoef(X) where X is a matrix returns a matrix of correlation
coefficients for each row of X.
corrcoef(x,y) where x and y are vectors returns the matrix or
correlation coefficients for x and y.
Numeric arrays can be real or complex
The correlation matrix is defined from the covariance matrix C as
r(i,j) = C[i,j] / (C[i,i]*C[j,j])
"""
if len(args) == 2:
X = transpose(array([args[0]] + [args[1]]))
elif len(args == 1):
X = args[0]
else:
raise RuntimeError, 'Only expecting 1 or 2 arguments'
C = cov(X)
d = resize(diagonal(C), (2, 1))
r = divide(C, sqrt(matrixmultiply(d, transpose(d))))[0, 1]
try:
return r.real
except AttributeError:
return r
def makeTreeArray(self, dec_list=None):
"""Makes an array with nodes in rows and descendants in columns.
A value of 1 indicates that the decendant is a descendant of that node/
A value of 0 indicates that it is not
also returns a list of nodes in the same order as they are listed
in the array"""
#get a list of internal nodes
node_list = [node for node in self.traverse() if node.Children]
node_list.sort()
#get a list of TerminalDescendants Data if one is not supplied
if not dec_list:
dec_list = [dec.Data for dec in self.TerminalDescendants]
dec_list.sort()
#make a blank array of the right dimensions to alter
result = resize(array([0]), (len(node_list), len(dec_list)))
#put 1 in the column for each child of each node
for i, node in enumerate(node_list):
children = [dec.Data for dec in node.TerminalDescendants]
for j, dec in enumerate(dec_list):
if dec in children:
result[i,j] = 1
return result, node_list
def reset_utils(self):
self.utility = resize(0.0, (self.num_squares_x, self.num_squares_y))
for e in self.pits:
self.utility[e] = -1.0
e = self.goal
self.utility[e] = 1.0
def RenderGradient(surf, topcolor, bottomcolor):
'''Creates a new 3d vertical gradient array.
This code was copied from the vgrade example'''
import pygame
from Numeric import array, repeat, resize, arange, \
Float, NewAxis, Int, UnsignedInt8
topcolor = array(topcolor, copy=0)
bottomcolor = array(bottomcolor, copy=0)
diff = bottomcolor - topcolor
width, height = surf.get_size()
# create array from 0.0 to 1.0 triplets
column = arange(height, typecode=Float)/height
column = repeat(column[:, NewAxis], [3], 1)
# create a single column of gradient
column = topcolor + (diff * column).astype(Int)
# make the column a 3d image column by adding X
column = column.astype(UnsignedInt8)[NewAxis,:,:]
#3d array into 2d array
column = pygame.surfarray.map_array(surf, column)
# stretch the column into a full image
return resize(column, (width, height))
def reset_frequencies(self):
self.frequencies = resize(0, (self.num_squares_x, self.num_squares_y))
def reset_reward(self):
self.reward = resize(0.0, (self.num_squares_x, self.num_squares_y))
self.reward[self.goal] = 100
for e in self.pits:
self.reward[e] = -50
def iagaussian(s,mu,sigma):
""" o Purpose
Generate a 2D Gaussian image.
o Synopsis
g = iagaussian(s,mu,sigma)
o Input
s: [rows columns]
mu: Mean vector. 2D point (x;y). Point of maximum value.
sigma: covariance matrix (square). [ Sx^2 Sxy; Syx Sy^2]
o Output
g:
o Description
A 2D Gaussian image is an image with a Gaussian distribution. It can be used to generate test patterns or Gaussian filters both for spatial and frequency domain. The integral of the gaussian function is 1.0.
o Examples
import Numeric
f = iagaussian([8,4], [3,1], [[1,0],[0,1]])
print Numeric.array2string(f, precision=4, suppress_small=1)
g = ianormalize(f, [0,255]).astype(Numeric.UnsignedInt8)
print g
f = iagaussian(100, 50, 10*10)
g = ianormalize(f, [0,1])
g,d = iaplot(g)
showfig(g)
f = iagaussian([50,50], [25,10], [[10*10,0],[0,20*20]])
g = ianormalize(f, [0,255]).astype(Numeric.UnsignedInt8)
iashow(g)
"""
from Numeric import asarray,product,arange,NewAxis,transpose,matrixmultiply,reshape,concatenate,resize,sum,zeros,Float,ravel,pi,sqrt,exp
from LinearAlgebra import inverse,determinant
if type(sigma).__name__ in ['int', 'float', 'complex']: sigma = [sigma]
s, mu, sigma = asarray(s), asarray(mu), asarray(sigma)
if (product(s) == max(s)):
x = arange(product(s))
d = x - mu
if len(d.shape) == 1:
tmp1 = d[:,NewAxis]
tmp3 = d
else:
tmp1 = transpose(d)
tmp3 = tmp1
if len(sigma) == 1:
tmp2 = 1./sigma
else:
tmp2 = inverse(sigma)
k = matrixmultiply(tmp1, tmp2) * tmp3
else:
aux = arange(product(s))
x, y = iaind2sub(s, aux)
xx = reshape(concatenate((x,y)), (2, product(x.shape)))
d = transpose(xx) - resize(reshape(mu,(len(mu),1)), (s[0]*s[1],len(mu)))
if len(sigma) == 1:
tmp = 1./sigma
else:
tmp = inverse(sigma)
k = matrixmultiply(d, tmp) * d
k = sum(transpose(k))
g = zeros(s, Float)
aux = ravel(g)
if len(sigma) == 1:
tmp = sigma
else:
tmp = determinant(sigma)
aux[:] = 1./(2*pi*sqrt(tmp)) * exp(-1./2 * k)
return g
def psd(x,
NFFT=256,
Fs=2,
detrend=detrend_none,
window=window_hamming,
noverlap=0):
"""
The power spectral density by Welches average periodogram method.
The vector x is divided into NFFT length segments. Each segment
is detrended by function detrend and windowed by function window.
noperlap gives the length of the overlap between segments. The
absolute(fft(segment))**2 of each segment are averaged to compute Pxx,
with a scaling to correct for power loss due to windowing. Fs is
the sampling frequency.
-- NFFT must be a power of 2
-- detrend and window are functions, unlike in matlab where they are
vectors.
-- if length x < NFFT, it will be zero padded to NFFT
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
"""
if NFFT % 2:
raise ValueError, 'NFFT must be a power of 2'
# zero pad x up to NFFT if it is shorter than NFFT
if len(x) < NFFT:
n = len(x)
x = resize(x, (NFFT, ))
x[n:] = 0
# for real x, ignore the negative frequencies
# if x.typecode()==Complex: numFreqs = NFFT
if any(numpy.iscomplex(x)): numFreqs = NFFT
else: numFreqs = NFFT // 2 + 1
# windowVals = window(ones((NFFT,),x.typecode()))
windowVals = window(numpy.ones(NFFT))
step = NFFT - noverlap
ind = range(0, len(x) - NFFT + 1, step)
n = len(ind)
# Pxx = zeros((numFreqs,n), Float)
Pxx = numpy.zeros([numFreqs, n])
# do the ffts of the slices
for i in range(n):
thisX = x[ind[i]:ind[i] + NFFT]
thisX = windowVals * detrend(thisX)
fx = absolute(fft(thisX))**2
#print("numFreqs={0:f}".format(numFreqs))
#print("len of fx slice={0:d}".format(len(fx[:int(numFreqs)])))
#print("len of destination in Pxx={0:d}")
Pxx[:, i] = fx[:int(numFreqs)]
# Scale the spectrum by the norm of the window to compensate for
# windowing loss; see Bendat & Piersol Sec 11.5.2
if n > 1: Pxx = mean(Pxx, 1)
Pxx = divide(Pxx, norm(windowVals)**2)
freqs = Fs / NFFT * arange(0, numFreqs)
return Pxx, freqs
def csd(x,
y,
NFFT=256,
Fs=2,
detrend=detrend_none,
window=window_hamming,
noverlap=0):
"""
The cross spectral density Pxy by Welches average periodogram
method. The vectors x and y are divided into NFFT length
segments. Each segment is detrended by function detrend and
windowed by function window. noverlap gives the length of the
overlap between segments. The product of the direct FFTs of x and
y are averaged over each segment to compute Pxy, with a scaling to
correct for power loss due to windowing. Fs is the sampling
frequency.
NFFT must be a power of 2
Refs:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
"""
if NFFT % 2:
raise ValueError, 'NFFT must be a power of 2'
# zero pad x and y up to NFFT if they are shorter than NFFT
if len(x) < NFFT:
n = len(x)
x = resize(x, (NFFT, ))
x[n:] = 0
if len(y) < NFFT:
n = len(y)
y = resize(y, (NFFT, ))
y[n:] = 0
# for real x, ignore the negative frequencies
# if x.typecode()==Complex: numFreqs = NFFT
if any(numpy.iscomplex(x)): numFreqs = NFFT
else: numFreqs = NFFT // 2 + 1
# windowVals = window(ones((NFFT,),x.typecode()))
windowVals = window(numpy.ones(NFFT))
step = NFFT - noverlap
ind = range(0, len(x) - NFFT + 1, step)
n = len(ind)
# Pxy = zeros((numFreqs,n), Complex)
Pxy = numpy.zeros([numFreqs, n])
# do the ffts of the slices
for i in range(n):
thisX = x[ind[i]:ind[i] + NFFT]
thisX = windowVals * detrend(thisX)
thisY = y[ind[i]:ind[i] + NFFT]
thisY = windowVals * detrend(thisY)
fx = fft(thisX)
fy = fft(thisY)
Pxy[:, i] = fy[:numFreqs] * conjugate(fx[:numFreqs])
# Scale the spectrum by the norm of the window to compensate for
# windowing loss; see Bendat & Piersol Sec 11.5.2
if n > 1: Pxy = mean(Pxy, 1)
Pxy = divide(Pxy, norm(windowVals)**2)
freqs = Fs / NFFT * arange(0, numFreqs)
return Pxy, freqs
def __init__(self, root, width, height):
Tkinter.Toplevel.__init__(self, root)
self.inaccessible = [(-1,-1),\
( 4, 2),( 5, 2),( 6, 2),( 4, 3),( 5, 3),( 6, 3),\
( 9, 2),(10, 2),(11, 2),( 9, 3),(10, 3),(11, 3),( 9, 4),(10, 4),(11, 4),\
(13, 1),(14, 1),(13, 2),(14, 2),(13, 3),(14, 3),(13, 4),(14, 4),\
(3,6),(4,6),(5,6),(3,7),(4,7),(5,7),(3,8),(4,8),(5,8),(3,9),(4,9),(5,9),\
(7,6),(8,6), (7,7),(8,7), (7,8),(8,8), \
(6,9),(7,9),(8,9), (6,10),(7,10),(8,10),(9,10),(10,10),(6,11),(7,11),\
(8,11),(9,11),(10,11),(6,12),(7,12),(8,12),(9,12),(6,13),(7,13),(8,13),\
(11,6),(12,6),(13,6),(11,7),(12,7),(13,7),(11,8),(12,8),(13,8),\
(0,11),(1,11),(2,11),(0,12),(1,12),(2,12),(0,13),(1,13),(2,13),(0,14),(1,14),(2,14)];
self.path_color = "#5ee563"
self.visited_color = "#c5c5c5"
self.current_pos_color = "#00AF32"
self.inaccessible_color= "black"
self.gridline_color = "black"
self.background_color = "white"
self.path = []
self.visited = []
self.pits = []
self.goal = []
self.goal_id = -1
self.pit_ids = []
# how many states ?
self.num_squares_x = 15
self.num_squares_y = 15
self.squares = resize( 0,(self.num_squares_x,self.num_squares_y));
# various object members
self.done = 0
self.quit = 0
self.root = root
self.width = width
self.height = height
self.complete = 0
# various tk objects
self.title("SymbolicSimulator: RLWorld")
self.canvas = Tkinter.Canvas(self,width=self.width,height=self.height,bg="black")
self.canvas.pack()
self.winfo_toplevel().protocol('WM_DELETE_WINDOW',self.destroy)
# set height and width of images
self.square_height = self.width / self.num_squares_x;
self.square_width = self.height / self.num_squares_y;
# goal image
goldFilename = pyrobotdir() + "/images/rlgoal.gif"
goldImage = Image.open(goldFilename)
goldImage = goldImage.resize( [self.square_height-2, self.square_width-2] )
self.goldImageTk = ImageTk.PhotoImage(goldImage)
# pit image
pitFilename = pyrobotdir() + "/images/rlpit.gif"
pitImage = Image.open(pitFilename)
pitImage = pitImage.resize( [self.square_height-2, self.square_width-2] )
self.pitImageTk = ImageTk.PhotoImage(pitImage, height=self.square_height, width=self.square_width)
for i in range(0, self.num_squares_x):
for j in range(0, self.num_squares_y):
self.squares[i][j] = self.canvas.create_rectangle( i*self.square_width, j*self.square_height,
(i+1)*self.square_width - 1, (j+1)*self.square_height - 1,
fill= self.background_color, tag = "square-%d-%d" % (i,j));
# initialize the world
self.initWorld()
self.resetStates()
# used by simulator
self.properties = ["location", "obstacles", "goal", "home", \
"final", "visited", "complete", \
"pits", "path"]
self.movements = ["up", "right", "down", "left"]
self.ports = [60000]
# start things off
self.redraw()
self.drawInaccessible()
def reset_frequencies(self):
self.frequencies = resize(0, (self.num_squares_x, self.num_squares_y))
def reset_reward(self):
self.reward = resize(0.0, (self.num_squares_x, self.num_squares_y))
self.reward[self.goal] = 100
for e in self.pits:
self.reward[e] = -50
def __init__(self, root, width, height):
Tkinter.Toplevel.__init__(self, root)
self.inaccessible = [(-1,-1),\
( 4, 2),( 5, 2),( 6, 2),( 4, 3),( 5, 3),( 6, 3),\
( 9, 2),(10, 2),(11, 2),( 9, 3),(10, 3),(11, 3),( 9, 4),(10, 4),(11, 4),\
(13, 1),(14, 1),(13, 2),(14, 2),(13, 3),(14, 3),(13, 4),(14, 4),\
(3,6),(4,6),(5,6),(3,7),(4,7),(5,7),(3,8),(4,8),(5,8),(3,9),(4,9),(5,9),\
(7,6),(8,6), (7,7),(8,7), (7,8),(8,8), \
(6,9),(7,9),(8,9), (6,10),(7,10),(8,10),(9,10),(10,10),(6,11),(7,11),\
(8,11),(9,11),(10,11),(6,12),(7,12),(8,12),(9,12),(6,13),(7,13),(8,13),\
(11,6),(12,6),(13,6),(11,7),(12,7),(13,7),(11,8),(12,8),(13,8),\
(0,11),(1,11),(2,11),(0,12),(1,12),(2,12),(0,13),(1,13),(2,13),(0,14),(1,14),(2,14)]
self.path_color = "#5ee563"
self.visited_color = "#c5c5c5"
self.current_pos_color = "#00AF32"
self.inaccessible_color = "black"
self.gridline_color = "black"
self.background_color = "white"
self.path = []
self.visited = []
self.pits = []
self.goal = []
self.goal_id = -1
self.pit_ids = []
# how many states ?
self.num_squares_x = 15
self.num_squares_y = 15
self.squares = resize(0, (self.num_squares_x, self.num_squares_y))
# various object members
self.done = 0
self.quit = 0
self.root = root
self.width = width
self.height = height
self.complete = 0
# various tk objects
self.title("SymbolicSimulator: RLWorld")
self.canvas = Tkinter.Canvas(self,
width=self.width,
height=self.height,
bg="black")
self.canvas.pack()
self.winfo_toplevel().protocol('WM_DELETE_WINDOW', self.destroy)
# set height and width of images
self.square_height = self.width / self.num_squares_x
self.square_width = self.height / self.num_squares_y
# goal image
goldFilename = os.environ["PYROBOT"] + "/images/rlgoal.gif"
goldImage = Image.open(goldFilename)
goldImage = goldImage.resize(
[self.square_height - 2, self.square_width - 2])
self.goldImageTk = ImageTk.PhotoImage(goldImage)
# pit image
pitFilename = os.environ["PYROBOT"] + "/images/rlpit.gif"
pitImage = Image.open(pitFilename)
pitImage = pitImage.resize(
[self.square_height - 2, self.square_width - 2])
self.pitImageTk = ImageTk.PhotoImage(pitImage,
height=self.square_height,
width=self.square_width)
for i in range(0, self.num_squares_x):
for j in range(0, self.num_squares_y):
self.squares[i][j] = self.canvas.create_rectangle(
i * self.square_width,
j * self.square_height, (i + 1) * self.square_width - 1,
(j + 1) * self.square_height - 1,
fill=self.background_color,
tag="square-%d-%d" % (i, j))
# initialize the world
self.initWorld()
self.resetStates()
# used by simulator
self.properties = ["location", "obstacles", "goal", "home", \
"final", "visited", "complete", \
"pits", "path"]
self.movements = ["up", "right", "down", "left"]
self.ports = [60000]
# start things off
self.redraw()
self.drawInaccessible()
