convolution

For an evaluation of a random forest regression, I am trying to improve a result using a moving average filter after fitting a model using a RandomForestRegressor for a dataset found in this link import pandas as pd import math import matplotlib import matplotlib.pyplot as plt import numpy as np from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.model_selection import GridSearchCV from sklearn.metrics import r2_score, mean_squared_error, make_scorer from sklearn.model_selection import train_test_split from math import sqrt from sklearn.cross_validation

I'm making a convolution filter for my project and I managed to make it for any size of matrix but as it gets bigger I noticed that not all bits are changed. Here are the pictures showing the problem: First one is the original Filter: Blur 9x9 Filter: EdgeDetection 9x9: As you can see, there is a little stripe that is never changed and as the matrix gets bigger, the stripe also gets bigger (in 3x3 it wasn't visible) My convolution matrix class: public class ConvMatrix { public int Factor = 1; public int Height, Width; public int Offset = 0; public int[,] Arr; //later I assign functions to set

I have a 1D input signal. I want to compute autocorrelation as the part of the neural net for further use inside the network. I need to perform convolution of input with input itself. To perform convolution in keras custom layer/ tensorflow. We need the following parameters data shape is "[batch, in_height, in_width, in_channels]", filter shape is "[filter_height, filter_width, in_channels, out_channels] There is no batch present in filter shape, which needs to be input in my case TensorFlow now has an auto_correlation function. It should be in release 1.6 . If you build from source you can

I have an existing classification model that was trained using theano's conv2d under theano.tensor.nnet. Now I have to use this model to do some sort of prediction in Java. I implement a simple convolution in Python(In the end, I will code it in Java) as per some documentation( https://developer.apple.com/Library/ios/documentation/Performance/Conceptual/vImage/ConvolutionOperations/ConvolutionOperations.html ). For example, for a 2*2 kernel (k11,k12,k21,k22), one of the areas under the kernel is (a11,a12,a21,a22). The convolution is performed by a11*k11 + a12*k12 + a21*k21 + a22*k22.

I am trying to do some (de)convolution with audio samples. I have one sample s and the same sample with some filters added on top of it s_f . Both samples are represented as numpy arrays. I want to deconvolve them in order to get an array that represents the isolated filter f . Once I do that I should be able to reproduce s_f using convolution of s and f . Here's the code: f = signal.deconvolve(s, s_f) convolved = signal.convolve(s, f) However, I get the following error on the second line: ValueError: in1 and in2 should have the same rank Does anyone know what am I doing wrong here? Thanks

I am working on a convolutional neural net that requires some parts of the a kernel weights to be untrainable. tf.nn.conv2d(x, W) takes in a trainable variable W as weights. How can I make some of the elements of W to be untrainable? Maybe you could have your trainable weights W1 , a mask M indicating where the trainable variables are, and a constant / untrainable weight matrix W2 , and use W = tf.multiply(W1, tf.cast(M, dtype=W1.dtype)) + tf.multiply(W2, tf.cast(tf.logical_not(M), dtype=W2.dtype)) 来源： https://stackoverflow.com/questions/44774672/tensorflow-trainable-variable-masking

I need to convolve two one dimensional signals, one has on average 500 points (This one is a Hanning window function), the other 125000. Per run, I need to apply three times the convolution operation. I already have an implementation running based on the documentation of scipy. You can see the code here if you want to (Delphi code ahead): function Convolve(const signal_1, signal_2 : ExtArray) : ExtArray; var capital_k : Integer; capital_m : Integer; smallest : Integer; y : ExtArray; n : Integer; k : Integer; lower, upper : Integer; begin capital_k := Length(signal_1) + Length(signal_2) - 1;