Remove that sum, each element of Cl is a float so you can’t possibly call sum on them: If you intend to invoke numpy’s broadcasting to perform the power operation then you don’t need to index the array.
(Note: this answer is based on a short blog post about einsum I wrote a while ago.) What does einsum do? Imagine that we have two multi-dimensional arrays, A and B. Now let’s suppose we want to… multiply A with B in a particular way to create new array of products; and then maybe sum this new array along particular axes; and then maybe transpose the axes of the new … Read more
You have a 0-dimensional array of object dtype. Making this array at all is probably a mistake, but if you want to use it anyway, you can extract the dictionary by indexing the array with a tuple of no indices: or by calling the array’s item method:
If you are using Anaconda3 then you should already have numpy installed. There is no reason to use pip. My guess is that the Anaconda distribution is possibly not on your path and you are picking up some other system python. You should run where python (or where python3) to see which one you are using. If the executable … Read more
The above 2 lines doesn’t work the way you expect. append() function of list doesn’t return anything. It adds data at the end of the list object through which the append() was called. So final_array & final_direction will be None. I think you just need to append data to final_array & final_direction. Like this
The Y array in your screenshot is not a 1D array, it’s a 2D array with 300 rows and 1 column, as indicated by its shape being (300, 1). To remove the extra dimension, you can slice the array as Y[:, 0]. To generally convert an n-dimensional array to 1D, you can use np.reshape(a, a.size). Another option for converting a 2D array … Read more
Broadcasting is really good for this: row_sums[:, numpy.newaxis] reshapes row_sums from being (3,) to being (3, 1). When you do a / b, a and b are broadcast against each other. You can learn more about broadcasting here or even better here.
A simple way to achieve this is by using np.convolve. The idea behind this is to leverage the way the discrete convolution is computed and use it to return a rolling mean. This can be done by convolving with a sequence of np.ones of a length equal to the sliding window length we want. In order to do so we could … Read more
The functionality of numpy.mean and tensorflow.reduce_mean are the same. They do the same thing. From the documentation, for numpy and tensorflow, you can see that. Lets look at an example, Output Here you can see that when axis(numpy) or reduction_indices(tensorflow) is 1, it computes mean across (3,4) and (5,6) and (6,7), so 1 defines across which axis the mean is computed. When it is 0, the … Read more
You can lookup the documentation and the source code of the deprecated function. In short, using Pillow (Image.resize) you can do: With skimage (skimage.transform.resize) you should get the same with: