gaussian | A JavaScript model of the normal distribution | Analytics library
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kandi X-RAY | gaussian Summary
A JavaScript model of the Normal (or Gaussian) distribution.
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gaussian Examples and Code Snippets
Lines of Code : 50License : Permissive (MIT License)
Copypublic static void gaussian(double a[][], int index[]) {
int n = index.length;
double c[] = new double[n];
// Initialize the index
for (int i = 0; i < n; ++i) {
index[i] = i;
}
// Find def gaussian(x, mu: float = 0.0, sigma: float = 1.0) -> int:
"""
>>> gaussian(1)
0.24197072451914337
>>> gaussian(24)
3.342714441794458e-126
>>> gaussian(1, 4, 2)
0.06475879783294587
&g def gauss_easter(year: int) -> datetime:
"""
Calculation Gregorian easter date for given year
>>> gauss_easter(2007)
datetime.datetime(2007, 4, 8, 0, 0)
>>> gauss_easter(2008)
datetime.datetime(2008, 3, import numpy as np
rng = np.random.default_rng()
N = 100000
n = 2
def f(x):
return np.power(x, n)
sample = f(rng.random(N)) # N samples of the function
m = np.mean(sample) # mean value of the sample, approaching integral v#
# The surface plot shows a two variable multivariate probability"
# density function. On the x-y plane are some samples of the random"
# vector and a contour plot illustrating the correlation, which in"
# this case is zero, i.e. a circl // 2. Use the margin convention practice
var margin = { top: 50, right: 50, bottom: 50, left: 50 },
width = window.innerWidth - margin.left - margin.right, // Use the window's width
height = window.innerHeight - margin.top - maconst W = 680, H = 200; // dimensions of canvas
const time = 400; // number of x tick values
const step = W/time; // time step
let data = []; // to store number of infected people
let count = 0; // steps counter
let pos, fy, c, infected, import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas import DataFrame
from sklearn import datasets
from sklearn.mixture import GaussianMixture
# load the iris dataset
iris = datasets.load_iris()
# seimport cv2
import numpy as np
# Load image, create blank mask, convert to grayscale, Gaussian blur
# then adaptive threshold to obtain a binary image
image = cv2.imread('1.jpg')
mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtC%Read sample EEG file (MATLAB built in sample).
load('ecgsig.mat');
E = ecgsig(1:end/100); %Take only 1% of data sample
T = zeros(size(E));
%Create an stripe of ones.
T(1+end*3/8:end*5/8) = 1;
%Smooth T - create smooth change by applyiCommunity Discussions
Trending Discussions on gaussian
QUESTION
I am trying to make a next-word prediction model with LSTM + Mixture Density Network Based on this implementation(https://www.katnoria.com/mdn/).
Input: 300-dimensional word vectors*window size(5) and 21-dimensional array(c) representing topic distribution of the document, used to train hidden initial states.
Output: mixing coefficient*num_gaussians, variance*num_gaussians, mean*num_gaussians*300(vector size)
x.shape, y.shape, c.shape with an experimental 161 obserbations gives me such:
(TensorShape([161, 5, 300]), TensorShape([161, 300]), TensorShape([161, 21]))
...ANSWER
Answered 2021-Jun-14 at 19:07for MDN model , the likelihood for each sample has to be calculated with all the Gaussians pdf , to do that I think you have to reshape your matrices ( y_true and mu) and take advantage of the broadcasting operation by adding 1 as the last dimension . e.g:
QUESTION
ANSWER
Answered 2021-Jun-09 at 03:01A little bit late, but here's an alternative solution for segmenting the tools. It involves converting the image to the CMYK color space and extracting the K (Key) component. This component can be thresholded to get a nice binary mask of the tools, the procedure is very straightforward:
- Convert the image to the CMYK color space
- Extract the K (Key) component
- Threshold the image via Otsu's thresholding
- Apply some morphology (a closing) to clean up the mask
- (Optional) Get bounding rectangles of all the tools
Let's see the code:
QUESTION
I have been running a model for the past 24h (optimization of Neural Network parameters by Gaussian process). Tonight, haven't waked up properly form, I hand accidentally switched the cell to markdown and the layout disappeared. Anyway, the code is still running but I don't if the rest of the cells would execute correctly. I like you to tell me if the remaining cells would run as attended or if I should stop and start the whole notebook over again?
...ANSWER
Answered 2021-Jun-11 at 07:57this morning the process was over and the code ran perfectly well. So, switching the jupyter notebook cell from code to markdown does not affect the remaining cells and their execution.
QUESTION
I am trying to blur of highest variance point from the image. I wrote code below. 1st part finds the variance of the image. I checked the resultant variance of an image and it is correct. (I used Lena's image) In 2nd part, I find the highest variance coordinates and send to this Function which finds gaussian blur. When I execute this code, it throws "C:\Tmp\blur_highest_variance.py", line 66, in sigma=15) numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'subtract' output from dtype('float64') to dtype('uint8') with casting rule 'same_kind'
I tried a few conversions between types but no avail. Can you show me some direction?
ANSWER
Answered 2021-Jun-10 at 18:48The error message tells us the line and the reason for the error:
Traceback (most recent call last):
File "C:\Tmp\blur_highest_variance.py", line 66, in sigma=15)
numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'subtract' output from dtype('float64') to dtype('uint8') with casting rule 'same_kind'
It is more simple to debug the code using intermediate variables:
For example, use an intermediate named gmask:
QUESTION
ANSWER
Answered 2021-Jun-08 at 07:34The typical reason for the fail is: wrong starting parameters. The formulae used here are somewhat those that would be used for a measurement statistic, while the data here has to be considered the according histogram. The use of n, hence, does not make sense. It is more like
QUESTION
Here's some minimal code:
...ANSWER
Answered 2021-Jun-08 at 07:23I believe you are running into this problem because curve_fit is also testing non-integer values of n, in which case your function gauss returns complex values when x.
I believe it would be easier to brute-force your way through every integer n and find the best parameters sig,c,x_o for each n.
For example, if you consider n = 0,1,2,... up to perhaps 50, there are very few options for n, and brute forcing is actually a decent method.
Looking at your data, it would be even better to only consider n as a multiple of 2 (unless your other data looks like n could be an odd integer).
Also, you should probably introduce some bounds for the other parameters, like it would be good to have sig>0, and you can safely set c>0 (that is, if all of your data looks like the minimal data you included in your question).
Here is what I did:
QUESTION
I am trying to run a code that has a GUI built with pyglet. but it gives this error. I have searched and found that I need to directly set the version of GLSL to be used by the code but I don't know how. would be happy if you helped me out with it.
...ANSWER
Answered 2021-Jun-08 at 07:05well it got solved!
just needed to add the directive #version 120 at the beginning of the shader like this:
QUESTION
ANSWER
Answered 2021-Jun-07 at 15:50When applying Gaussian blur filter on a mask, it blurs only the borders of the mask.
Example:
QUESTION
I have an array: [1,1.2,1.4,1.5.....] with 1000 elements. I would like to randomly pick a value from these choices using a weighted gaussian probability with a given mean. For example, I have set mean value of 25. So the weight of choices is a gaussian function which has mean around 25, i.e the most of the numbers picked are around 25.
Duplicate of this question but using python instead of javascript. Probability curve is something like this:
Background Info I am trying to fit a curve on some data which has asymmetric error bars and I cannot find any python module to do such fitting. So I am doing a Monte-Carlo simulation where I randomly pick x and y data points from the error range with data values as mean and repeat it some (let's say) 1000 times and optimize the mean square error.
This is how my data looks like:
...ANSWER
Answered 2021-Jun-03 at 20:22Couldn't you use take advantage of the numpy random sample method?
QUESTION
Can anyone tell me what is wrong with this code? It is from https://jakevdp.github.io/blog/2012/09/05/quantum-python/ . Everything in it worked out except the title of the plot.I can't figure it out.
but when the code is run, it polts this
Here is the code given:-
...ANSWER
Answered 2021-Jun-04 at 18:23The problem is resolved when blit=False, though it may slow down your animation.
Just quoting from a previous answer:
"Possible solutions are:
Put the title inside the axes.
Don't use blitting"
See: How to update plot title with matplotlib using animation?
You also need ffmpeg installed. There are other answers on stackoverflow that help you through that installation. But for this script, here are my recommended new lines you need to add, assuming you're using Windows:
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