## Padding multi-dimensional array to its max shape

For some machine learning purpose, I need to work with sequences with different lengths. To be able to process efficiently those sequences, I need to process them in batches of size size_batch. A batch typically has 4 dimensions and I want to convert it to a numpy’s ndarray with 4 dimensions. For each sequence, I need to pad with some defined pad_value such that each element has the same size: the maximal size.

For example with 3 dimensional input:

[[[0, 1, 2],   [3],   [4, 5]],  [[6]],  [[7, 8],   [9]]] 

desired output for pad_value -1 is:

[[[0, 1, 2],   [3, -1, -1],   [4, 5, -1]],  [[6, -1, -1],   [-1, -1, -1],   [-1, -1, -1]]  [[7, 8, -1],   [9, -1, -1],   [-1, -1, -1]]] 

which has shape (3, 3, 3). For this problem, one can suppose there are no empty list in the input. Here is the solution I came up with:

import numpy as np import itertools as it from typing import List  def pad(array: List, pad_value: np.int32, dtype: type = np.int32) -> np.ndarray:     """ Pads a nested list to the max shape and fill empty values with pad_value      :param array: high dimensional list to be padded     :param pad_value: value appended to     :param dtype: type of the output     :return: padded copy of param array     """     # Get max shape     def get_max_shape(arr, ax=0, dims=[]):         try:             if ax >= len(dims):                 dims.append(len(arr))             else:                 dims[ax] = max(dims[ax], len(arr))              for i in arr:                 get_max_shape(i, ax+1, dims)         except TypeError:  # On non iterable / lengthless objects (leaves)             pass          return dims      dims = get_max_shape(array)      # Pad values     def get_item(arr, idx):         while True:             i, *idx = idx             arr = arr[i]             if not idx:                 break         return arr      r = np.zeros(dims, dtype=dtype) + pad_value     for idx in it.product(*map(range, dims)):         # idx run though all possible tuple of indices that might          # contain a value in array         try:             r[idx] = get_item(array, idx)         except IndexError:             continue      return r 

It does not feel really pythonic but does the job. Is there any better way to do it I should know ? I think I might be able to improve its speed by doing smart breaks in the last loop but I haven’t dug much yet.

Thanks !

## Array multidimensional como retirar um carácter de um determinado index?

O código abaixo é resultado da leitura de um arquivo CSV, que o transformo em Array para trata-lo para criar um novo CSV pra poder ser importado com um novo formato. Quero retirar os – que estão nos números de telefone / celular.

Sabemos que nos telefones / celulares existem sempre 4 dígitos antes do -, mas não sei como faze-lo para retirar.

Exemplo:

array   0 =>      array (size=4)       0 => string 'Nome/Razão Social'       1 => string 'Nome Fantasia'       2 => string 'Tel.'       3 => string 'Cel.'   1 =>      array (size=4)       0 => string 'XXX XXXXX XXXXXXXX'       1 => string ''       2 => string '9123-4321'       3 => string '7123-4312' 

## How to transform an array into multidimensional array

I have an array like:

Array {     [one] => 1     [two] => 2     [three] => 3     [four] => 4     ... } 

but I need to transfer this into a multidimensional array like that:

Array {     [one] => Array         {             [two] => Array                 {                     [three] => Array                         {                             [four] => Array                             {                              ...                             }                         }                 }         } } 

Any help would be appreciated.

## Como cambiar el valor de un elemento dentro de un arreglo multidimensional en javascript

Buen día

Me surgió algo extraño trabajando con arreglos en javascript, probablemente este omitiendo algo investigue al respecto pero no encontré algo que me ayudara

var arreglo = [['0'],['1'],['2'],['3']]; 

Luego por realizo un push para copiar el ultimo elemento en cuestion

arreglo.push(arreglo[arreglo.length -1]); 

y la estructura del arreglo quedaria de la siguiente forma

arreglo = [['0'],['1'],['2'],['3'],['3']]; 

al querer cambiar el ultimo elemento del arreglo despues del push

arreglo[arreglo.length -1][0] = 'finDeArray'; 

no se por que cambia tambien el elemento del indice [3][0] y termina asi

arreglo = [['0'],['1'],['2'],['finDeArray'],['finDeArray']]; 

cuando busco que termine asi

arreglo = [['0'],['1'],['2'],['3'],['finDeArray']]; 

cualquier tipo de ayuda es agradecida

Saludos

## multidimensional change of variables

If $$\mathbf{X}=(X_1,…,X_n)$$ s.t $$X_i$$‘s are iid r.vs with $$X_i\sim Ga(\alpha_i,\beta_i)$$. Let $$S=\sum_{j=1}^\infty X_j$$ and $$R_i={X_i\over S}$$ s.t $$\mathbf{R}=(R_1,…,R_n)$$. I have read that the density of $$f_{\mathbf{R},S}(r_1,…,r_n,s)$$ is $$f_{\mathbf{R},S}(r_1,…,r_n,s)=f_{\mathbf{X}}(r_1s,…,r_ns)s^{n-1}.$$ How did we make the variable change from $$n$$ to $$n+1$$ variables and should $$|J|=s^{n}$$?

## Convexity of help function of a convex multidimensional function

Let $$f: \mathbb{R}^N \longrightarrow \mathbb{R}$$ be convex. How can I show that $$g_{x,y}: \mathbb{R} \longrightarrow \mathbb{R}, g_{xy}(t):= f((1-t)x+ty)$$ is convex, too?

## I want to build multidimensional array at Twig

I want to build multidimensional array at Twig.

In php

 $attributes['attributes']['class'] = array();$  attributes['attributes']['class'][] = 'innerTab';  \$  attributes['html'] = TRUE;

Same thing I want to build in Twig. I tried but not getting proper one . Please check my code below

 {% set ctn = 0 %}          {% for key, value in menu.menu %} {% set attributes = {'attributes':'class'} %} {% set attributes = attributes|merge( [ { (ctn) :'innerTab'} ,  {'html' : true} ]  ) %}  {% set ctn = ctn+1  %}  {% endfor %}

As a result I get :

array(3) { [“attributes”]=> string(5) “class” [0]=> array(1) { [0]=> string(8) “innerTab” } [1]=> array(1) { [“html”]=> bool(true) } } array(3) { [“attributes”]=> string(5) “class” [0]=> array(1) { [1]=> string(8) “innerTab” } [1]=> array(1) { [“html”]=> bool(true) } } array(3) { [“attributes”]=> string(5) “class” [0]=> array(1) { [2]=> string(8) “innerTab” } [1]=> array(1) { [“html”]=> bool(true) } }

## Python multidimensional Lookup interpolation

To interpolate within a dataset, I want to create a multidimensional Lookup table in python. In this case, the tree input arrays x,y and z are given together with the output quantity a.

I was able to visualize the impact of x and y with a 2D heatmap:

xi = np.linspace(np.amin(x),np.amax(x),100) yi = np.linspace(np.amin(y),np.amax(y),100)  zi = griddata((x, y), a, (xi[None,:], yi[:,None]), method='linear')  CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k') CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.bwr_r) plt.colorbar() 

However, my goal is not visualization. In the end I want to pass some x,y and z values to the Lookup and get an interpolated value of a.

I’ve allready found something related here, but the proposed function did not work. Is there any library I’ve missed so far or a suitable way to perform a multidimensional interpolation?

## Find and add to an associative array in an unknown multidimensional array

(Could probably do eith a better title.)

Hi.

I am trying to create a self populating tree by adding arrays to arrays. What I am having trouble with is finding an associatetive key within the array and then adding new content to it (a new array).

Hope thats clear enough.

Thanks.

## Multidimensional Fourier transform: inner product in exponential?

I have a basic and not very deep understanding of continuous and discrete Fourier transforms in one dimension.

In a multidimensional Fourier transform, the exponent of $$e$$ includes the inner product of the arguments of the original function and the transformed function. I’m having trouble finding an explanation of why this is the right way to extend the idea of a Fourier transform to more than one dimension. What is the intuition, or perhaps a more formal reason for this idea? In one dimension, the original function’s and transformed function’s arguments are multiplied, and using an inner product in a multidimensional case is one natural extension of that idea, but that’s not enough to justify it.

I see that using the inner product in the exponential means that the integral or sum (for a discrete Fourier transform) is over a product of $$\cos x_i + i \sin x_i$$ sums, but I am not sure why that makes sense, or even whether that’s a useful way to think about it. (I can picture waves in two real dimensions, and multiplying one-dimensional wave equations kind of feels like a good way to represent that, but I still don’t have a clear understanding.)

Pointers to texts as well as explanations here would be welcome.