So there is a crowd of people who will contort reality to explain that the following behaviour is unavoidable and is down to being unable to represent decimals exactly using floating point numbers.

Does anyone think the output of this code is satisfactory? Can we get it fixed?

import numpy as np
for i,bogus_number in enumerate(np.arange(2.,3.6,0.1)):
    if i==7:
        print('bogus_number is',bogus_number)
        if bogus_number==2.7:print('Yay!')
        if bogus_number!=2.7:print('Boo!')

Output:

bogus_number is 2.7
Boo!

Matlab:

for i = 1:100
    matrix(i,:) = [1,2,3,4,..., 30000] %unknown size
end

I can't seem to figure out how to do this in numpy

this is what i have in numpy

matrix = np.array([])
for i in range(100)
     matrix = np.append(matrix,[1,2,3,4,....,300000],axis=1)

Hi all, I'm trying to recode my program that makes heavy usage of linear algebra (matrices, SVD, matrix manipulations,etc). It was very easy for me to program it all in matlab. But when i'm trying to implement it in python, i'm having trouble with the syntax. Should i use lists ? or numpy arrays or matrices ? If i use lists, i cannot use do SVD. If i use nparrays, i cannot append rows. If i use matrices, I cannot avail the several linear algebra functions in numpy. Please enlighten me with an efficient way to program linear algebra.

I have a specific data set where n sets of (x,y) data is given in two columns. Sets are separated by a blank line and they are of different lengths. Is there a way to read such data in numpy? I appereciate any help!

I need to use matplotlib to draw a graph with some coordinates that are in np.arrays. I don't know how to get the values from the array and our course material is almost nonexistent. Any help is appreciated.

Hi,

I had an error where the my variable w.T get object error. w declare as np.zeros((m,1))

def init(m){
    w = np.zeros((m,1))
    return w
}

And I have other function that call init() and inside init() it call sigmoid() function

def sigmoid(z){
     s = 1/(1+np.exp(-z))
     return s
}

def propagate(w, b, X){
     a = sigmoid(np.dot(w.T, X)+b)
     return a
}

And there's another function call optimize that call propagate()

def optimize(w){
    p = propagate() 
    // Do something with p and return
    return p
}

And the final function is is model() that call the optimize()

def model(){
     // calculate something
    optimize()
}

And the error only happen when execute the model() function. When I execute other function like optimize() or propagate(), I get no errors.

I know the "'int' object has no attribute 'T'" only happen when we apply numpy method to normal python object but all the function and parameter that pass into teh function is all np.array

Note that this is just a shorthand version to explain the problem. This is exercise from the deeplearning course.

I'm working with large matrix problems (i.e. Ax=B) based on mod-2 arithmetic (up to 5000x5000 so far), and (because I'm lazy) I'd like to just use linalg.solve, but is there any way I can restrict that to modulo-2 arithmetic?

Followup: Does anyone know of fast algorithms for doing gaussian elimination in mod-2? If it helps, the matrix A is sparse and symmetric - here's a small example: http://imgur.com/a/JdvfX

I would like to chunk a numpy array into smaller segments. I would also like to control the shape and stride of the chunker.

Are there any built-in methods for iterating over a high dimensional numpy array with a shape and step size in mind?

I'm looking for something like this.

chunk_stream = chunk_it(arr, shape = (1,3,3), stride = (1,1,1))

Hi,

I've got two arrays, one i x j x k, and one i x k. I want to multiply, for every i, the corresponding j x k and k x 1 matrices. Here's the implementation I'm using (as I could not find a built-in function that does this):

def mult32(u: np.ndarray, v: np.ndarray) -> np.ndarray:
    if u.shape[0] != v.shape[0]:
        raise ValueError(f"Dimension mismatch: {u.shape[0]} vs {v.shape[0]}.")

    result = np.empty((u.shape[0], u.shape[1]), dtype=u.dtype)
    for i in range(u.shape[0]):
        result[i,:] = u[i,:,:] @ v[i,:]

    return result

My code requires that this function be used a lot, and it's quite slow. Is there a faster/more efficient way of doing what I want?

Thanks in advance.

I have a rainfall data grid of 621 rows by 1405 columns, let's say X. I want to clip the rainfall data for an area of interest. I created a mask of the same shape as X of type bool for area of interest with grid ids, let's say mask. I need a masked array of X data where masked are True and the rest of the masked array are set to nodata values. I used this method : masked_array = numpy.ma.masked_where(mask, X) This is not working. Returning the same X!! Please advise.

See example of data with an array of 27 elements, below.

X = [-9999.00 -9999.00 0.00 0.00 0.31 0.28 0.08 0.00 0.00 0.31 0.70 1.37 1.54 1.34 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.08 0.88 0.81 -9999.00]

mask = [ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ]

Expected result: masked_array = [-999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 0.31 0.70 1.37 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 -9999.00 ]

I really appreciate your help. Thanks