In Python,
I created a 10 x 20 zero-matrix, called X:
X = numpy.zeros((10, 20))
I have another 50 x 20 matrix called A.
I want to let the 4th row of matrix X take the value of the 47th row of matrix A.
How can I write this in Python?
Note: if X is a list, then I could just write X.append () However, here X is not a list...then how can I do this?
Or, if I just have a list that contains 20 numbers, how can I let the 4th row of matrix X equal to that list of 20 numbers?
Thank you!
I'll try to answer this. So the correct syntax for selecting an entire row in numpy is
M[row_number, :]
The : part just selects the entire row in a shorthand way.
There is also a possibility of letting it go from some index to the end by using m:, where m is some known index.
If you want to go between to known indices, then we will use
M[row_number, m:n]
where m < n.
You can equate the rows/columns of a 2D-array only if they are of the same dimension.
I won't give you the exact piece of code that you'll need, but hopefully now you can figure it out using the above piece of code.
I will also suggest playing around with all kinds of matrices, and their operations like replacing some elements, columns, and rows, as well as playing with matrix multiplication until you get the hang of it.
Some useful, commands include
numpy.random.rand(m, n) # will create a matrix of dimension m x n with pseudo-random numbers between 0 and 1
numpy.random.rand(m, n) # will create a matrix of dimension m x n with pseudo-random numbers between -1 and 1
numpy.eye(m) # will create a m x m identity matrix.
numpy.ones((m, n))
And make sure to read through the docs.
Good luck! And let your Python journey be a fun one. :)
Related
I will have an interview with a company which like the hackerearth.com. I don't know how to work and doing the code perfectly. Could you help me with the following example?
This is the example for the .hackerearth.com, however, I don't know that I should consider the constraint in the code? can I use a package like NumPy? or I should only use the basic calculation with my self? Could you check my response and let me know the problem with that? Thank you so much
Input Format:
First line of input consists of an integer N denoting the number of elements in the array A.
Second line consists of N space separated integers denoting the array elements.
Output Format:
The only line of output consists of the value of x.
Input Constraints:
1<N<100
1<A[i]<100
explanation:
An initial sum of array is 1+2+3+4+5=15
When we update all elements to 4, the sum of array which is greater than 15 .
Note that if we had updated the array elements to 3, which is not greater than 15 . So, 4 is the minimum value to which array elements need to be updated.
# Write your code here
import numpy as np
A= [1, 2, 3,4,5]
for i in range(1, max(A)+1):
old = sum(A)
new = sum(i*np.ones(len(A)))
diff = new-old
if diff>0:
print(i)
break
Well this isn't Code Review stack exchange, but:
You don't say how to calculate x. It seems to be something to do with finding an average value, but no-one can judge your code without know what it's trying to do. A web search suggests it is this:
Fredo is assigned a new task today. He is given an array A containing N integers. His task is to update all elements of array to some minimum value x , that is, ; such that sum of this new array is strictly greater than the sum of the initial array. Note that x should be as minimum as possible such that sum of the new array is greater than the sum of the initial array.
Given that the task starts by accepting input, it's important that your program does this part.
N = int(input()) # you can put a prompt string in here, but may conflict with limited output
A = list(map(int,input().split()))
# might need input checks
# might need range checks
# might check that A has exactly N values
you don't need to recalculate old = sum(A) every time around your search loop
calculation of new doesn't need a sum at all - it's just new = i * len(A)
there's no point in checking values of i at or below min(A)
your search will fail if all values of A are the same (try it), because you never look above max(A)
These remarks apply to your approach; a more efficient search would be binary chop, and there is also a mathematical way to go straight to the answer from sum(A) without any searching:
x = sum(A) // len(A) + 1
You don't need numpy or looping for this. Get the average of the array elements, then get the next higher integer from this.
N = 5
A = [1, 2, 3, 4, 5]
total = sum(A)
avg = A/N # not checking for zero-divide because conditions say N > 1
x = floor(avg + 1)
print(x)
Adding 1 is necessary to make the new sum greater than the original sum when the average is an exact integer (e.g. 15/5 == 3).
Python and coding beginner here. I started learning python a couple of days ago, no prior coding experience, and I've started learning about functions. Since python is really useful for mathematical operations, I'm trying to tie this with what I'm learning in my linear algebra class. So here's the question. Beware a lot of reading ahead!
I'm trying to multiply two random matrices using python, without numpy (otherwise I can use numpy.dot and numpy.matrix). If we have 2 matrices X and Y, of dimensions axb and bxc respectively, matrix multiplication only works if the columns of X and the rows of Y are equal. In order to write a program that can do the matrix multiplication, here's what I tried.
First I defined my function as def mat.mul(A,B), with dimensions axb and bxc respectively. I then have the matrix Z as the product of the matrix A and B, which will be empty, Z = []. Here's where my thought process is a bit wobbly. I think that first I need a for loop that iterates through the columns of A, for a in range(0, len(A)):, followed by another for loop that iterates through the rows of A, for b in range(len(0, X[0])) , followed by another for loop to iterate through the columns of B, for c in range(0, len(Y)) and finally a last for loop that iterates through the rows of Y, for d in range(0, len(Y[0])). Now I should have the product matrix Z but I'm not how I should write it. Would it be Z += X[i] * Y[d]?
Sorry for the long explanation, I just thought I'd share my thought process as well.
I have a matrix NxM.
N is big enough N >> 10000.
I wonder if there is an algorithm to mix all the lines of a matrix to get a 100 matrix for example. My matrices C must not be identical.
Thoughts?
So, do you want to keep the shape of the matrix and just shuffle the rows or do you want to get subsets of the matrix?
For the first case I think the permutation algorithm from numpy could be your choice. Just create a permutation of a index list, like Souin propose.
For the second case just use the numpy choice funtion (also from the random module) without replacement if I understood your needs correctly.
For alpha and k fixed integers with i < k also fixed, I am trying to encode a sum of the form
where all the x and y variables are known beforehand. (this is essentially the alpha coordinate of a big iterated matrix-vector multiplication)
For a normal sum varying over one index I usually create a 1d array A and set A[i] equal to the i indexed entry of the sum then use sum(A), but in the above instance the entries of the innermost sum depend on the indices in the previous sum, which in turn depend on the indices in the sum before that, all the way back out to the first sum which prevents me using this tact in a straightforward manner.
I tried making a 2D array B of appropriate length and width and setting the 0 row to be the entries in the innermost sum, then the 1 row as the entries in the next sum times sum(np.transpose(B),0) and so on, but the value of the first sum (of row 0) needs to vary with each entry in row 1 since that sum still has indices dependent on our position in row 1, so on and so forth all the way up to sum k-i.
A sum which allows for a 'variable' filled in by each position of the array it's summing through would thusly do the trick, but I can't find anything along these lines in numpy and my attempts to hack one together have thus far failed -- my intuition says there is a solution that involves summing along the axes of a k-i dimensional array, but I haven't been able to make this precise yet. Any assistance is greatly appreciated.
One simple attempt to hard-code something like this would be:
for j0 in range(0,n0):
for j1 in range(0,n1):
....
Edit: (a vectorized version)
You could do something like this: (I didn't test it)
temp = np.ones(n[k-i])
for j in range(0,k-i):
temp = x[:n[k-i-1-j],:n[k-i-j]].T#(y[:n[k-i-j]]*temp)
result = x[alpha,:n[0]]#(y[:n[0]]*temp)
The basic idea is that you try to press it into a matrix-vector form. (note that this is python3 syntax)
Edit: You should note that you need to change the "k-1" to where the innermost sum is (I just did it for all sums up to index k-i)
This is 95% identical to #sehigle's answer, but includes a generic N vector:
def nested_sum(XX, Y, N, alpha):
intermediate = np.ones(N[-1], dtype=XX.dtype)
for n1, n2 in zip(N[-2::-1], N[:0:-1]):
intermediate = np.sum(XX[:n1, :n2] * Y[:n2] * intermediate, axis=1)
return np.sum(XX[alpha, :N[0]] * Y[:N[0]] * intermediate)
Similarly, I have no knowledge of the expression, so I'm not sure how to build appropriate tests. But it runs :\
I'm working on Euler Project, problem 11, which involves finding the greatest product of all possible combinations of four adjacent numbers in a grid. I've split the numbers into a nested list and used a list comprehension to slice the relevant numbers, like this:
if x+4 <= len(matrix[x]): #check right
my_slice = [int(matrix[x][n]) for n in range(y,y+4)]
...and so on for the other cardinal directions. So far, so good. But when I get to the diagonals things get problematic. I tried to use two ranges like this:
if x+4 <= len(matrix[x]) and y-4 >=0:# check up, right
my_slice = [int(matrix[m][n]) for m,n in ((range(x,x+4)),range(y,y+4))]
But this yields the following error:
<ipython-input-53-e7c3ebf29401> in <listcomp>(.0)
48 if x+4 <= len(matrix[x]) and y-4 >=0:# check up, right
---> 49 my_slice = [int(matrix[m][n]) for m,n in ((range(x,x+4)),range(y,y+4))]
ValueError: too many values to unpack (expected 2)
My desired indices for x,y values of [0,0] would be ['0,0','1,1','2,2','3,3']. This does not seem all that different for using the enumerate function to iterate over a list, but clearly I'm missing something.
P.S. My apologies for my terrible variable nomenclature, I'm a work in progress.
You do not need to use two ranges, simply use one and apply it twice:
my_slice = [int(matrix[m][m-x+y]) for m in range(x,x+4)]
Since your n is supposed to be attached to range(y,y+4) we know that there will always be a difference of y-x between m and n. So instead of using two variables, we can counter the difference ourselves.
Or in case you still wish to use two range(..) constructs, you can use zip(..) which takes a list of generators, consumes them concurrently and emits tuples:
my_slice = [int(matrix[m][n]) for m,n in zip(range(x,x+4),range(y,y+4))]
But I think this will not improve performance because of the tuple packing and unpacking overhead.
[int(matrix[x+d][n+d]) for d in range(4)] for one diagonal.
[int(matrix[x+d][n-d]) for d in range(4)] for the other.
Btw, better use standard matrix index names, i.e., row i and column j. Not x and y. It's confusing. I think you even confused yourself, as for example your if x+4 <= len(matrix[x]) tests x against the second dimension length but uses it in the first dimension. Huh?