I want to calculate the Determinant of a Singular Matrix (which has a 0 determinant) with Numpy and when I print the determinant it shows a really small number
If we want to search for the optimal parameters theta for a linear regression model by using the normal equation with: theta = inv(X^T * X) * X^T * y one step
For some rectangular we can select all indices in a 2D array very efficiently: arr[y:y+height, x:x+width] ...where (x, y) is the upper-left corner of the rec
Let a = np.array([1, 1, 1,1,1,1]) b = np.array([2,2,2]) be two numpy arrays. Then let c = [a]+[b]+[b] clearly, c has duplicated elements b. Now I wish to
Launching pyspark in client mode. bin/pyspark --master yarn-client --num-executors 60 The import numpy on the shell goes fine but it fails in the kmeans. Someho
I have an array and want to find the average between 2 numbers and add an additional element between the 2 numbers. For example, if I start with x = np.array([1
I'm trying to create a convolution kernel, and the middle is going to be 1.5. Unfortunately I keep running in to ideas on how to do that. I'm trying to create s
x = np.arange(0,2,0.5) valeur = 2*x if valeur <= 0.6: print ("this works") else: print ("valeur is too high") here is the error I get: if vale
I'm trying to generate a sine wave of a given frequency for a given duration and then write it into a .wav file. I'm using numpy's sin function and scipy's wavf
I have this kind of dataframe, and I'm looking to get for each row the last column name equals to 1 Here is an example of my dataframe col1 col2
I currently have a numpy array or RBG tuples that I want to convert to a PIL image and save. Currently I'm doing the following: final = Image.fromarray(im_arr,
The gaussian_kde function in scipy.stats has a function evaluate that can returns the value of the PDF of an input point. I'm trying to use gaussian_kde to esti
In short, the problem I encounter is this: aa = np.arange(-1., 0.001, 0.01) aa[-1] Out[16]: 8.8817841970012523e-16 In reality, this cause a series problem si
Hopefully this is a quick and easy question that is not a repeat. I am looking for a built in numpy function (though it could also be a part of another library)
I need to find the first and the last element of a numpy.ndarray which are above a specified threshold. I found the following solution, which works, but it look
I want to change list to tensor with tf.convert_to_tensor, data is following: data=[ array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
Suppose I have the following function: def f(x,y): return x*y How do I apply the funtion to each element in an NxM 2D numpy array using the multiprocessi
I'm using redis on an AI project. The idea is to have multiple environment simulators running policies on a lot of cpu cores. The simulators write experience
I was completing the first course of the deeplearning specialization, where the first programming assignment was to build a logistic regression model from scrat
Suppose there exists a numpy array, data. I am trying to do the equivalent of the following cv2.imwrite(filename, data) with open(filename, 'rb') as fp: da