We Keep Coding

k means algorithm python

Whenever k = 2, the code runs in a loop
if k > 2 it sets all, but one of the centroids location to 0,0
I've reviewed it a couple of times , and it doesn't seem like there are any errors probably some sort of logic flaw. The code starts by having a class and its methods which initiate the centroids, calculate the Euclidean distance, and reassign centroids to the average positions of the points that are in the cluster. It then runs a loop that consists of reassigning and calculating distance until a list of the assignments are equal and then plots it.
class Kmeans:
def __init__(self, K, dataset, centroids, sorting):
self.K = K
self.dataset = dataset
self.centroids = centroids
self.sorting = sorting
#sets starting position of centroids
def initializeCentroids(self):
bigX = 0
bigY = 0
self.centroids = []
for i in self.dataset:
if i[0] > bigX:
bigX = i[0]
if i[1] > bigY:
bigY = i[1]
for q in range(self.K):
self.centroids.append([random.randint(0, bigX), random.randint(0, bigY)])
plt.scatter((self.centroids[0][0], self.centroids[1][0]), (self.centroids[0][1], self.centroids[1][1]))
return self.centroids
#calculates euclidean distance
def calcDistance(self):
self.sorting = []
for w in self.dataset:
print(w)
distances = []
counter = 0
for centr in self.centroids:
distances.append(math.sqrt(abs((centr[0] - w[0] * centr[0] - w[0]) + (centr[1] - w[1] * centr[1] - w[1]))))
counter += 1
if counter > 0:
try:
if distances[0] > distances[1]:
distances.pop(0)
if distances[1] > distances[0]:
distances.pop(1)
counter -= 1
except IndexError:
pass
self.sorting.append([w, counter, distances[0]])
return self.sorting
def reassignCentroids(self):
counter3 = 1
for r in range(len(self.centroids)):
positionsX = []
positionsY = []
for t in self.sorting:
if t[1] == counter3:
positionsX.append(t[0][0])
positionsY.append(t[0][1])
population = len(positionsY)
if population == 0:
population = 1
self.centroids.append([sum(positionsX) / population, sum(positionsY) / population])
counter3 += 1
self.centroids.pop(0)
return
k = 4
dataSetSize = input("Enter the amount of tuples you want generated: ")
data_set = []
for o in range(int(dataSetSize)):
data_set.append((random.randint(0, 1000), random.randint(0, 1000)))
attempt = Kmeans(k, data_set, 0, 0)
attempt.initializeCentroids()
xvals = []
yvals = []
sortCompare = []
# plots
for p in data_set:
xvals.append(p[0])
yvals.append(p[1])
running = True
while running:
if len(sortCompare) > 1:
centroidChoice0 = []
centroidChoice1 = []
for p in sortCompare[0]:
centroidChoice0.append(p[1])
for d in sortCompare[1]:
centroidChoice1.append(d[1])
print(centroidChoice1)
print(attempt.centroids)
if centroidChoice1 == centroidChoice0:
running = False
for m in attempt.centroids:
plt.scatter((attempt.centroids[0][0], attempt.centroids[1][0]), (attempt.centroids[0][1], attempt.centroids[1][1]))
running = False
sortCompare.pop(0)
attempt.calcDistance()
sortCompare.append(attempt.sorting)
attempt.reassignCentroids()

Solving a maze by eliminating junctions

We are trying to make a program that solves any maze by recognising all junctions and eliminating the ones that do not lead to the entrance. We managed to create such a program but we are struggling to get the dots to connect to create a proper path. Does anybody have an idea how to do this because we are out of clues...Picture of the result, but the dots aren't connect by a line
The maze is basically a (n)x(n) grid in a numpy array with walls (true) and paths (false) see:picture of maze as seen from the variable explorer
import numpy as np
import maze_utils as mu
import matplotlib.pyplot as plt
size = 101
maze, start = mu.make_maze(size)
start = [start[1],start[0]]
#------------------------------------------------------------------------------
def junctions_finder(maze, size, start):
junctions = [start]
end = []
for y, row in enumerate(maze):
for x, column in enumerate(row):
if maze[x,y] == False:
if x == 0 or x == (size-1) or y == 0 or y == (size-1):
junctions.append([y,x])
end.append([y,x])
while True:
if x+1 < size and y+1 < size and\
maze[x+1,y] == False and maze[x,y+1] == False\
or x+1 < size and y-1 > 0 and\
maze[x+1,y] == False and maze[x,y-1] == False\
or x-1 > 0 and y-1 > 0 and\
maze[(x-1),y] == False and maze[x,(y-1)] == False\
or x-1 > 0 and y+1 < size and\
maze[(x-1),y] == False and maze[x,(y+1)] == False:
junctions.append([y,x])
break
else:
break
return junctions, end
#------------------------------------------------------------------------------
def eliminate_coor(junctions, end, start):
eliminated = []
for row in junctions:
a = row[1]
b = row[0]
connections = 0
U = False
D = False
L = False
R = False
UW = False
DW = False
LW = False
RW = False
SE = False
if row == start or row == end[0]:
connections = 2
SE = True
for i in range(1,size):
if SE == False:
if a+i <= size-1 and DW == False and D == False:
if maze[a+i, b] == True:
DW = True
else:
for coor in junctions:
if [coor[1],coor[0]] == [a+i,b]:
connections = connections + 1
D = True
if a-i >= 0 and UW == False and U == False:
if maze[a-i, b] == True:
UW = True
else:
for coor in junctions:
if [coor[1],coor[0]] == [a-i,b]:
connections = connections + 1
U = True
if b+i <= size-1 and RW == False and R == False:
if maze[a, b+i] == True:
RW = True
else:
for coor in junctions:
if [coor[1],coor[0]] == [a,b+i]:
connections = connections + 1
R = True
if b-i >= 0 and LW == False and L == False:
if maze[a, b-i] == True:
LW = True
else:
for coor in junctions:
if [coor[1],coor[0]] == [a,b-i]:
connections = connections + 1
L = True
if connections < 2:
eliminated.append([b,a])
return eliminated
#------------------------------------------------------------------------------
def junction_remover(junctions, eliminated):
counter = 0
for index, row in enumerate(junctions):
for erow in (eliminated):
if erow == row:
junctions[index] = -1
counter = counter + 1
for times in range(counter):
junctions.remove(-1)
return junctions, counter
#------------------------------------------------------------------------------
junctions, end = junctions_finder(maze, size, start)
counter = 1
while counter > 0:
eliminated = eliminate_coor(junctions, end, start)
junctions, counter = junction_remover(junctions, eliminated)
start = [start[1],start[0]]
junctions.pop(0)
pyjunc = np.array(junctions)
mu.plot_maze(maze, start=start)
plt.plot(pyjunc[:,0], pyjunc[:,1], 'o')

plt.plot(pyjunc[:,0], pyjunc[:,1]) would connect the dots... Or did you mean you have a bug you can't trace? In your picture it seems there's a beginning, but no end, so it retraces back to the beginning?

plt.plot(pyjunc[:,0], pyjunc[:,1], 'o')
plots the data points in the list with the circle marker 'o'. But you haven't defined a linestyle.
The quickest way to do this is to add it the format shorthand:
plt.plot(pyjunc[:,0], pyjunc[:,1], 'o-')
Which says to use a circle marker and a solid line '-'.
Expanding that out to how matplotlib interprets it, you could write:
plt.plot(pyjunc[:,0], pyjunc[:,1], marker='o', linestyle='-')
You can see the full documentation for plt.plot more ways to customise your plot

word search puzzle generator written in python 3 gets caught in an infinite loop

So I started to create a word search generator in Python 3 with the least complexity possible. I ended up with the following code and hope to find out where I need to improve.
The issue:
The main issue is that the code might get stuck in an infinite loop trying to put the last word in the puzzle. My aim is to make the code more efficient by avoiding that.
The main file (wsg.py) and the word database (n_words.txt) are available at:
https://github.com/mannysayah/wordsearchpuzzle_py3
Here's how wsg.py looks:
import random
import string
import numpy as np
import numpy.ma as ma
# How many meaningful words should take up the puzzle
make_up_total = 0.70
# Grid size
width = 10
height = 10
# Probabilities
ph = 0.3
pd = 0.3
pv = 0.4
total_words_letters = int(width * height * make_up_total)
filename = 'n_words.txt'
with open(filename) as f:
data = f.readlines()
# remove whitespace characters like `\n` at the end of each line
data = [x.strip() for x in data]
list_of_all_words = []
for x in data:
if len(x) < width and len(x) < height:
list_of_all_words.append(x)
data = list_of_all_words
total_take = 0
word_list = []
while total_take < total_words_letters - 4:
item = random.choice(data)
if len(item) < total_words_letters - total_take:
word_list.append(item)
total_take += len(item)
print(word_list)
puzzle = np.zeros((width, height)).astype(int).astype(str)
directions = [[1,0],[0,1],[1,1]]
def existing_word(d,word):
xsize = width if d[0] == 0 else width - len(word)
ysize = width if d[1] == 0 else height - len(word)
x = random.randrange(0,xsize)
y = random.randrange(0,ysize)
temp_word = []
for i in range(0,len(word)):
temp_word.append(str(puzzle[y+d[1]*i][x+d[0]*i]))
return("".join(temp_word),x,y)
def compare_them(w1, w2):
comp = []
for i,j in zip(w1, w2):
if j == "0":
comp.append("Zero")
else:
if i == j:
comp.append("Match")
else:
comp.append(False)
if ( comp.count("Match") <= 1 and comp.count(False) == 0 ):
return True
else:
return False
def fill_with_rand(puzzle,w,h):
mask = ([puzzle == "0"])
new_puzzle = np.copy(puzzle)
alpha_puzzle = np.copy([[random.choice(string.ascii_uppercase) for i in range(0,w)] for j in range(0,h)])
new_puzzle[tuple(mask)] = alpha_puzzle[tuple(mask)]
return new_puzzle
for word in word_list:
while (True):
word = random.choice([word, word[::-1]]) # word can be backwards
d = np.random.choice([0,1,2],p=[ph,pv,pd]) # Take a random choice of [0,1,2]
d = directions[d] # now take that out of [directions]
from_puzzle = existing_word(d, word) # now take an empty space from the puzzle with the same length as the word
x = from_puzzle[1]
y = from_puzzle[2]
if compare_them(word, from_puzzle[0]):
for i in range(0,len(word)):
puzzle[y+d[1]*i][x+d[0]*i] = word[i]
break
print(fill_with_rand(puzzle, width, height))

How do I get my data in my heatmap?

I've programmed Conways Game of Life in Python and now I'm trying to display the simple data that it gives me as an output in a heat map.
This is my current code:
from Tkinter import *
import matplotlib.pyplot as plt
import time
import numpy as np
import random
size_x = 100
size_y = 10
# create the matrices
cell = [[0 for row in range(0, size_y)] for col in range(0, size_x)]
live = [[0 for row in range(0, size_y)] for col in range(0, size_x)]
temp = [[0 for row in range(0, size_y)] for col in range(0, size_x)]
# process and draw the next frame
def frame():
process()
draw()
root.after(100, frame)
# load the initial data
def load(initial=0.5):
for y in range(0, size_y):
for x in range(0, size_x):
if random.random()<initial: live[x][y] = 1
temp[x][y] = 0
# Applying rules
def process():
for y in range(0, size_y):
for x in range(0, size_x):
lives = live_neighbors(x,y)
if live[x][y] == 1:
if lives < 2 or lives > 3:
temp[x][y] = 0
else:
temp[x][y] = 1
if live[x][y] == 0:
if lives == 3:
temp[x][y] = 1
else:
temp[x][y] = 0
for y in range(0, size_y):
for x in range(0, size_x):
live[x][y] = temp[x][y]
# live = temp
# Count live neighbors
def live_neighbors(a,b):
lives = 0
if live[a][(b+1)%size_y] == 1: lives += 1
if live[a][(b-1)%size_y] == 1: lives += 1
if live[(a+1)%size_x][b] == 1: lives += 1
if live[(a+1)%size_x][(b+1)%size_y] == 1: lives += 1
if live[(a+1)%size_x][(b-1)%size_y] == 1: lives += 1
if live[(a-1)%size_x][b] == 1: lives += 1
if live[(a-1)%size_x][(b+1)%size_y] == 1: lives += 1
if live[(a-1)%size_x][(b-1)%size_y] == 1: lives += 1
return lives
# Draw all cells
def draw():
nLiving = 0
nDead = 0
for y in range(size_y):
for x in range(size_x):
if live[x][y]==0:
canvas.itemconfig(cell[x][y], fill="black")
nDead+=1
if live[x][y]==1:
canvas.itemconfig(cell[x][y], fill="white")
nLiving+=1
print nLiving,nDead
# count cells
def count():
nLiving = 0
nDead = 0
for y in range(size_y):
for x in range(size_x):
if live[x][y]==0:
nDead+=1
if live[x][y]==1:
nLiving+=1
z = nLiving / 10.0
print z,
print "%"
def one_game(initial):
load(initial)
for gen in range(1, 101):
print str(gen) + ":",
count()
process()
def many_games():
numbers = range(1,51)
for initial in numbers:
print initial/100.0
one_game(initial/100.0)
many_games()
#one_game(0.5)
The code for making a normal heat map with given input would be:
fig, ax = plt.subplots(1)
x = np.array( [[11,12,13], [21,22,23], [31,32,33]] )
p = ax.pcolormesh(x)
fig.colorbar(p)
plt.show()
How do I get my data (which in this case would be, the generations, the value which initializes the one_game() function, and nLiving) into an array?

I'm not 100% sure this is what you're intending, but it produced a pretty output heat map :)
def count():
nLiving = 0
nDead = 0
for y in range(size_y):
for x in range(size_x):
if live[x][y]==0:
nDead+=1
if live[x][y]==1:
nLiving+=1
z = nLiving / 10.0
print("nLiving over ten is: ", z,)
print("%")
return nLiving
def one_game(initial):
load(initial)
gen_array = []
for gen in range(1, 101):
print("Gen: ", str(gen) + ":",)
nLiving = count()
process()
gen_array.append(nLiving)
return gen_array
def many_games():
gen_output = []
numbers = range(1,51)
for initial in numbers:
print(initial/100.0)
gen_array = one_game(initial/100.0)
gen_output.append(gen_array)
return gen_output
gen_output = many_games()
#one_game(0.5)
fig, ax = plt.subplots(1)
x = np.array( gen_output )
p = ax.pcolormesh(x)
fig.colorbar(p)
plt.show()
That is just code modified from your count function to the end of the file. Basically you just need to return the output from the functions that you're calling into the right kind of data structures, I think...

Too many values to unpack with python

I have a little problem with Python.
I'm try to write an application for DCM standard who some slice and draw the final model.
This is my code:
from lar import *
from scipy import *
import scipy
import numpy as np
from time import time
from pngstack2array3d import pngstack2array3d
colors = 2
theColors = []
DEBUG = False
MAX_CHAINS = colors
# It is VERY important that the below parameter values
# correspond exactly to each other !!
# ------------------------------------------------------------
MAX_CHUNKS = 75
imageHeight, imageWidth = 250,250 # Dx, Dy
# configuration parameters
# ------------------------------------------------------------
beginImageStack = 430
endImage = beginImageStack
nx = ny = 50
imageDx = imageDy = 50
count = 0
# ------------------------------------------------------------
# Utility toolbox
# ------------------------------------------------------------
def ind(x,y): return x + (nx+1) * (y + (ny+1) )
def invertIndex(nx,ny):
nx,ny = nx+1,ny+1
def invertIndex0(offset):
a0, b0 = offset / nx, offset % nx
a1, b1 = a0 / ny, a0 % ny
return b0,b1
return invertIndex0
def invertPiece(nx,ny):
def invertIndex0(offset):
a0, b0 = offset / nx, offset % nx
a1, b1 = a0 / ny, a0 % ny
return b0,b1
return invertIndex0
# ------------------------------------------------------------
# computation of d-chain generators (d-cells)
# ------------------------------------------------------------
# cubic cell complex
# ------------------------------------------------------------
def the3Dcell(coords):
x,y= coords
return [ind(x,y),ind(x+1,y),ind(x,y+1),ind(x+1,y+1)]
# construction of vertex coordinates (nx * ny )
# ------------------------------------------------------------
V = [[x,y] for y in range(ny+1) for x in range(nx+1) ]
if __name__=="__main__" and DEBUG == True:
print "\nV =", V
# construction of CV relation (nx * ny)
# ------------------------------------------------------------
CV = [the3Dcell([x,y]) for y in range(ny) for x in range(nx)]
if __name__=="__main__" and DEBUG == True:
print "\nCV =", CV
#hpc = EXPLODE(1.2,1.2,1.2)(MKPOLS((V,CV[:500]+CV[-500:])))
#box = SKELETON(1)(BOX([1,2,3])(hpc))
#VIEW(STRUCT([box,hpc]))
# construction of FV relation (nx * ny )
# ------------------------------------------------------------
FV = []
v2coords = invertIndex(nx,ny)
for h in range(len(V)):
x,y= v2coords(h)
if (x < nx) and (y < ny): FV.append([h,ind(x+1,y),ind(x,y+1),ind(x+1,y+1)])
if __name__=="__main__" and DEBUG == True:
print "\nFV =",FV
#hpc = EXPLODE(1.2,1.2,1.2)(MKPOLS((V,FV[:500]+FV[-500:])))
#box = SKELETON(1)(BOX([1,2,3])(hpc))
#VIEW(STRUCT([box,hpc]))
# construction of EV relation (nx * ny )
# ------------------------------------------------------------
EV = []
v2coords = invertIndex(nx,ny)
for h in range(len(V)):
x,y = v2coords(h)
if x < nx: EV.append([h,ind(x+1,y)])
if y < ny: EV.append([h,ind(x,y+1)])
if __name__=="__main__" and DEBUG == True:
print "\nEV =",EV
#hpc = EXPLODE(1.2,1.2,1.2)(MKPOLS((V,EV[:500]+EV[-500:])))
#box = SKELETON(1)(BOX([1,2,3])(hpc))
#VIEW(STRUCT([box,hpc]))
# ------------------------------------------------------------
# computation of boundary operators (∂3 and ∂2s)
# ------------------------------------------------------------
"""
# computation of the 2D boundary complex of the image space
# ------------------------------------------------------------
Fx0V, Ex0V = [],[] # x == 0
Fx1V, Ex1V = [],[] # x == nx-1
Fy0V, Ey0V = [],[] # y == 0
Fy1V, Ey1V = [],[] # y == ny-1
v2coords = invertIndex(nx,ny)
for h in range(len(V)):
x,y = v2coords(h)
if (y == 0):
if x < nx: Ey0V.append([h,ind(x+1,y)])
if (x < nx):
Fy0V.append([h,ind(x+1,y),ind(x,y)])
elif (y == ny):
if x < nx: Ey1V.append([h,ind(x+1,y)])
if (x < nx):
Fy1V.append([h,ind(x+1,y),ind(x,y)])
if (x == 0):
if y < ny: Ex0V.append([h,ind(x,y+1)])
if (y < ny):
Fx0V.append([h,ind(x,y+1),ind(x,y)])
elif (x == nx):
if y < ny: Ex1V.append([h,ind(x,y+1)])
if (y < ny):
Fx1V.append([h,ind(x,y+1),ind(x,y)])
FbV = Fy0V+Fy1V+Fx0V+Fx1V
EbV = Ey0V+Ey1V+Ex0V+Ex1V
"""
"""
if __name__=="__main__" and DEBUG == True:
hpc = EXPLODE(1.2,1.2,1.2)(MKPOLS((V,FbV)))
VIEW(hpc)
hpc = EXPLODE(1.2,1.2,1.2)(MKPOLS((V,EbV)))
VIEW(hpc)
"""
# computation of the ∂2 operator on the boundary space
# ------------------------------------------------------------
print "start partial_2_b computation"
#partial_2_b = larBoundary(EbV,FbV)
print "end partial_2_b computation"
# computation of ∂3 operator on the image space
# ------------------------------------------------------------
print "start partial_3 computation"
partial_3 = larBoundary(FV,CV)
print "end partial_3 computation"
# ------------------------------------------------------------
# input from volume image (test: 250 x 250 x 250)
# ------------------------------------------------------------
out = []
Nx,Ny = imageHeight/imageDx, imageWidth/imageDx
segFaces = set(["Fy0V","Fy1V","Fx0V","Fx1V"])
for inputIteration in range(imageWidth/imageDx):
startImage = endImage
endImage = startImage + imageDy
xEnd, yEnd = 0,0
theImage,colors,theColors = pngstack2array3d('SLICES2/', startImage, endImage, colors)
print "\ntheColors =",theColors
theColors = theColors.reshape(1,2)
background = max(theColors[0])
foreground = min(theColors[0])
print "\n(background,foreground) =",(background,foreground)
if __name__=="__main__" and DEBUG == True:
print "\nstartImage, endImage =", (startImage, endImage)
for i in range(imageHeight/imageDx):
for j in range(imageWidth/imageDy):
xStart, yStart = i * imageDx, j * imageDy
xEnd, yEnd = xStart+imageDx, yStart+imageDy
image = theImage[:, xStart:xEnd, yStart:yEnd]
nx,ny = image.shape
if __name__=="__main__" and DEBUG == True:
print "\n\tsubimage count =",count
print "\txStart, yStart =", (xStart, yStart)
print "\txEnd, yEnd =", (xEnd, yEnd)
print "\timage.shape",image.shape
# ------------------------------------------------------------
# image elaboration (chunck: 50 x 50)
# ------------------------------------------------------------
"""
# Computation of (local) boundary to be removed by pieces
# ------------------------------------------------------------
if pieceCoords[0] == 0: boundaryPlanes += ["Fx0V"]
elif pieceCoords[0] == Nx-1: boundaryPlanes += ["Fx1V"]
if pieceCoords[1] == 0: boundaryPlanes += ["Fy0V"]
elif pieceCoords[1] == Ny-1: boundaryPlanes += ["Fy1V"]
"""
#if __name__=="__main__" and DEBUG == True:
#planesToRemove = list(segFaces.difference(boundaryPlanes))
#FVtoRemove = CAT(map(eval,planesToRemove))
count += 1
# compute a quotient complex of chains with constant field
# ------------------------------------------------------------
chains2D = [[] for k in range(colors)]
def addr(x,y): return x + (nx) * (y + (ny))
for x in range(nx):
for y in range(ny):
if (image[x,y] == background):
chains2D[1].append(addr(x,y))
else:
chains2D[0].append(addr(x,y))
#if __name__=="__main__" and DEBUG == True:
#print "\nchains3D =\n", chains3D
# compute the boundary complex of the quotient cell
# ------------------------------------------------------------
objectBoundaryChain = larBoundaryChain(partial_3,chains2D[1])
b2cells = csrChainToCellList(objectBoundaryChain)
sup_cell_boundary = MKPOLS((V,[FV[f] for f in b2cells]))
# remove the (local) boundary (shared with the piece boundary) from the quotient cell
# ------------------------------------------------------------
"""
cellIntersection = matrixProduct(csrCreate([FV[f] for f in b2cells]),csrCreate(FVtoRemove).T)
#print "\ncellIntersection =", cellIntersection
cooCellInt = cellIntersection.tocoo()
b2cells = [cooCellInt.row[k] for k,val in enumerate(cooCellInt.data) if val >= 4]
"""
# ------------------------------------------------------------
# visualize the generated model
# ------------------------------------------------------------
print "xStart, yStart =", xStart, yStart
if __name__=="__main__":
sup_cell_boundary = MKPOLS((V,[FV[f] for f in b2cells]))
if sup_cell_boundary != []:
out += [T([1,2])([xStart,yStart]) (STRUCT(sup_cell_boundary))]
if count == MAX_CHUNKS:
VIEW(STRUCT(out))
# ------------------------------------------------------------
# interrupt the cycle of image elaboration
# ------------------------------------------------------------
if count == MAX_CHUNKS: break
if count == MAX_CHUNKS: break
if count == MAX_CHUNKS: break
And this is the error take from the terminal :
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-2e498c6090a0> in <module>()
213
214 image = theImage[:, xStart:xEnd, yStart:yEnd]
--> 215 nx,ny = image.shape
216
217 if __name__=="__main__" and DEBUG == True:
ValueError: too many values to unpack
Someone can help me to solve this issue????

Based on the line:
image = theImage[:, xStart:xEnd, yStart:yEnd]
image is a 3d array, not a 2d array (it appears to be multiple slices of an image), with the 2nd and 3rd dimensions representing x and y respectively. Thus, if you want to get its dimensions you'll need to unpack it into three dimensions, something like:
nslice, nx, ny = image.shape