NumPy Array: Minesweeper - substituting random items - arrays

I am at the beginning of an attempt to make a "minesweeper" game. I have an 8 x 8 array of 0's. I would like to substitute 8 random 0's within the array with the value 1 (to represent "mines"). I have no clue where to begin. Here is my code:
import numpy as np
import sys
import random
a = np.array([(0, 0, 0, 0, 0, 0, 0, 0),
(0, 0, 0, 0, 0, 0, 0, 0),
(0, 0, 0, 0, 0, 0, 0, 0),
(0, 0, 0, 0, 0, 0, 0, 0),
(0, 0, 0, 0, 0, 0, 0, 0),
(0, 0, 0, 0, 0, 0, 0, 0),
(0, 0, 0, 0, 0, 0, 0, 0),
(0, 0, 0, 0, 0, 0, 0, 0)])
for random.item in a:
item.replace(1)
print(a)
row = int(input("row "))
column = int(input("column "))
print(a[row - 1, column - 1])
How do I replace 8 random 0's within the array with 1's?


Use np.random.choice without replacement option -
In [3]: a # input array of all zeros
Out[3]:
array([[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]])
# Generate unique flattened indices and on a flattened view of
# input array assign those as 1s
In [8]: a.flat[np.random.choice(a.size,8,replace=False)] = 1
# Verify results
In [9]: a
Out[9]:
array([[0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]])

Related

Finding distance between every point in a matrix

def create_zero_matrix(n,m):
return [[0 for i in range(m)] for j in range(n)]
def m_tight_print(m):
for i in range(len(m)):
line = ''
for j in range(len(m[0])):
line += str(m[i][j])
print(line)
def pd_map(r,c,sites):
blank = create_zero_matrix(r,c)
for count, site in enumerate(sites):
blank[site[0]][site[1]] = count #locating the shops
Hello, how do I calculate the distance of every point in my matrix from a specific point such as [1,3] (point 0) or [4,7] (point 1) or [7,2] (point 2) and change that particular value such that it shows the point that is closest to that particular coordinate?
>>> pprint(pizzaMap)
[[0, 0, 0, 0, 0, 0, 0, 'X', 1, 1]
[0, 0, 0, 0, 0, 0, 0, 1, 1, 1]
[0, 0, 0, 0, 0, 0, 0, 1, 1, 1]
[0, 0, 0, 0, 0, 0, 'X', 1, 1, 1]
['X', 0, 0, 0, 0, 0, 1, 1, 1, 1]
[2, 2, 2, 2, 2, 'X', 1, 1, 1, 1]
[2, 2, 2, 2, 2, 2, 1, 1, 1, 1]
[2, 2, 2, 2, 2, 2, 2, 1, 1, 1]
[2, 2, 2, 2, 2, 2, 2, 1, 1, 1]
[2, 2, 2, 2, 2, 2, 2, 'X', 1, 1]]
This above is the test case but I can only get this from my code.
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 2, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
```.
I tried to use numpy.argwhere() but in the end I could not do it. What code must I use such that I am able to get my test case?

Stack vertically 5 2D-arrays in diagonal to build a whole 2d-array

l have 5 adjacency matrices (nump arrays) : A, B, C, D, E. each of dimension [20,20].
Given A, B, C, D, E, l would like to build F which stacks the 5 adjacency matrices. Since we have 5 2D arrays of [20,20] then F is of dimension [20*5,20*5] as follow :
F=np.zeros((100,100))
F=[
[A,0,0,0,...,0],
[0,...,B,...,0],
[0,...,..,C,0],
[0,.........D,..,0],
[0,...........,E],
]
such that :
A is indexed at F[0][:20]
B is indexed at F[1][20:40]
C is indexed at F[2][40:60]
D is indexed at F[3][60:80]
E is indexed at F[4][80:100]
What is the efficient numpy way to do that for larage number of adjacency matrices ?. Let's, we have n adjacency matrices to stack in a diagonal of new 2D array of [n*20,n*20]

You could use scipy.sparse.block_diag:
>>> AtoE = np.add.outer(np.arange(5, 10), np.zeros((3, 3), int))
>>> scipy.sparse.block_diag(AtoE).A
array([[5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9, 9],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9, 9],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9, 9]], dtype=int64)
Sparse storage may be a good idea, anyway.
Alternatively, here is a more direct method in case you definitely want to use dense arrays:
>>> A = AtoE[0]
>>> N, N = A.shape
>>> k = len(AtoE)
>>> out = np.zeros((k, N, k, N), A.dtype)
>>> np.einsum('ijik->ijk', out)[...] = AtoE
>>> out.reshape(k*N, k*N)
array([[5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 6, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 7, 7, 7, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8, 8, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9, 9],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9, 9],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9, 9]])

Insert values in 2d numpy array

I am stuck on this simple issue but I can't seem to figure it out, I have a diagonal array:
N = [1,2,3,4,5,6,7,8,9]
A = numpy.diag(N)
And I have a list of row and column indices such as this:
B = [[1,0],[2,1],[3,2]]
I want to insert a value of 1 in A given the location from B, it helps to think of A as a 2-D matrix and B the set of coordinates I want to insert the value A in.
I tried to use the numpy.put but it doesn't seem to allow me to access a 2d array and I don't know how to think about it in a for-loop sense.
The desired answer would look like this:
A = [[1,0,0,0,0,0,0,0,0],[1,2,0,0,0,0,0,0,0],[0,1,3,0,0,0,0,0,0],[0,0,0,4,0,0,0,0,0],...,[0,0,0,0,0,0,0,0,9]]
Any help is appreciated

Maybe for loop
for x in B:
A[x[0],x[1]]=1
A
Out[189]:
array([[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 3, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 4, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 5, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 6, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 7, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 8, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 9]])

You need to group the first and the second coordinates together:
I, J = zip(*B)
or
I, J = numpy.transpose(B)
Then you can index A directly
A[I, J] = 1

Make B a numpy array:
B = np.array(b)
Then just index using the first and second columns:
A[B[:, 0], B[:, 1]] = 1
array([[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 3, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 4, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 5, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 6, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 7, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 8, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 9]])

How to find the number of elements of a two-dimensional array, read from a file?

I have a file. Inside the file I have stored a two-dimensional array, something like this:
[[0, 0, 1, 0, 1, 0, 1, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 1, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 0], [0, 1, 1, 0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
Lengths of arrays can vary and they are not always 10 elements long.
I read the array from the file using this method:
map = IO.readlines("test.txt")
and when i print the result using:
map.each {|x| puts "#{x}"}
the output is what I expect it to be. But if I try to get the row length using:
puts map[0].length
I get 320 instead of 10 (which is what I expect).
Can someone explain me why am I getting 320 instead of 10 ?

Instead of IO#readlines you should use JSON#parse since it’s a valid json:
require 'json'
JSON.parse(File.read("test.txt"))
#⇒ [[0, 0, 1, 0, 1, 0, 1, 0, 1, 0],
# [0, 0, 0, 0, 0, 0, 1, 1, 0, 0],
# [0, 0, 0, 0, 1, 1, 1, 1, 0, 0],
# [0, 0, 0, 1, 0, 0, 1, 0, 0, 0],
# [0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 1, 1, 1, 0],
# [0, 1, 1, 0, 1, 0, 1, 0, 1, 0],
# [0, 1, 0, 0, 0, 0, 0, 1, 0, 0],
# [0, 0, 0, 1, 0, 0, 0, 1, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]

"Array index is less than one" error at Winbugs

I am working on a multilevel differential item functioning model using WinBUGS package. I have successfully built simpler models, but I have also gotten the error "array index is less than one". I'd be very pleased if you could help.
# Model
Model
{
for (l in 1:50){
y[l] ~ dbern(p[l])
logit(p[l])<- u2[stu[l]] - beta[x[l]] + gamma[tea[l], x[l]]*grp[l] + alpha1[x[l]]*geo[l] +
alpha2[x[l]]*conf[l] + alpha3[x[l]]*ses[l]
}
for (t in 1:10){
for (i in 1:10){
gamma[t,i] ~ dnorm(gamma.hat[t,i], tau.gamma[i])
gamma.hat[t,i]<-pi1[i] + pi2[i]*inq[t]
}
}
# fixed effect prior
for (i in 1:10){
beta[i] ~ dnorm(0, .0001)
alpha1[i] ~ dnorm(0, .0001)
alpha2[i] ~ dnorm(0, .0001)
alpha3[i] ~ dnorm(0, .0001)
pi1[i] ~ dnorm(0, .0001)
pi2[i] ~ dnorm(0, .0001)
}
# Random effect prior
for (s in 1:5){
u2[s] ~ dnorm(0,tau.u2)
}
tau.u2 <- pow(sigma.u2, -2)
sigma.u2 ~ dunif (0, 100)
for (i in 1:10){
tau.gamma[i] <- pow(sigma.gamma[i],-2)
sigma.gamma[i] ~ dunif(0,100)
}
}
# Data
list(y=c(0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0), ses=c(0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1), conf=c(1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1), geo=c(1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1), grp=c(1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1), inq=c(1, 3, 2, 1, 3, 2, 3, 2, 3, 2), stu=c(1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0), tea=c( 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1), x=c(3, 2, 2, 1, 3, 2, 3, 3, 2, 2, 2, 3, 2, 3, 1, 3, 2, 2, 3, 2, 2, 3, 3, 2, 3, 2, 1, 2, 2, 1, 2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 2, 2, 3, 2, 3, 2))
#Initital values
list(beta=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), alpha1=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), alpha2=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), alpha3=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), sigma.gamma=(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), u2=c(0, 0, 0, 0, 0), pi0=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), pi1=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), sigma.u2=1, gamma=structure(
.Data=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim=c(10, 10)))

Indexes start from 1, not 0, in BUGS. The variables stu and tea are used for indexing, but they take the values 0,1 - they should be 1,2 instead.

Something going on with x. It is one shorter in length than the other variables, perhaps you are missing an observation.

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