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Dotted glass I am trying to do automatic image segmentation of different regions of a 2D MR image based on pixel intensity values. The first step is to implement a Gaussian mixture model on the histogram of the image. I need to plot the resulting Gaussian obta
Dotted glass I am trying to do automatic image segmentation of different regions of a 2D MR image based on pixel intensity values. The first step is to implement a Gaussian mixture model on the histogram of the image. I need to plot the resulting Gaussian obta
Anjum Sayed I've been following PyMC3's Gaussian Mixture Model example here: https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/gaussian_mixture_model.ipynb , and it works perfectly with artificial datasets . I've tried using real datasets, but I'm
ninja I want to cluster a binary image using GMM (Gaussian Mixture Model) and also want to plot the cluster centroids on the binary image itself. I used this as a reference : http://in.mathworks.com/help/stats/gaussian-mixture-models.html Here is my initial co
ninja I want to cluster a binary image using GMM (Gaussian Mixture Model) and also want to plot the cluster centroids on the binary image itself. I used this as a reference : http://in.mathworks.com/help/stats/gaussian-mixture-models.html Here is my initial co
Benjamin Doughty I am interested in fitting a 2-component Gaussian mixture model to the data shown below. However, since I'm plotting log-transformed counts here, normalized to be between 0-1, the maximum value my data will take is 0. When I try to do a naive
Newkid I want to perform cross validation on my Gaussian mixture model. Currently, my cross_validationapproach using sklearn is as follows. clf = GaussianMixture(n_components=len(np.unique(y)), covariance_type='full')
cv_ortho = cross_validate(clf, parameters_
Dentist_Not edible I have some time series data that looks like this: x <- c(0.5833, 0.95041, 1.722, 3.1928, 3.941, 5.1202, 6.2125, 5.8828,
4.3406, 5.1353, 3.8468, 4.233, 5.8468, 6.1872, 6.1245, 7.6262,
8.6887, 7.7549, 6.9805, 4.3217, 3.0347, 2.4026, 1.9317,
Yufeng I am really new to python and GMM. I recently learned GMM and tried to implement the code from here I have some problems running the gmm.sample() method: gmm16 = GaussianMixture(n_components=16, covariance_type='full', random_state=0)
Xnew = gmm16.s
kind Lite: If I have a MoG model with n components, each component has its own weight w^n. I have a sample. I wish to calculate the probability of drawing samples from the MoG. I can easily evaluate individual Gaussians, but I don't know how to consider their
Gabriele Pompa I'm fairly new to scikit-lear and GMM in general...I have some questions about the fit quality of Gaussian mixture models in python (scikit-learn). I have an array of data that you can find in DATA HERE to match a GMM of n=2 components . As a be
Yufeng I am really new to python and GMM. I recently learned GMM and tried to implement the code from here I have some problems running the gmm.sample() method: gmm16 = GaussianMixture(n_components=16, covariance_type='full', random_state=0)
Xnew = gmm16.s
Avpenn I have images that I want to subdivide using a Gaussian mixture model scikit-learn. Some images have labels, so I want to use a lot of prior information. I would like to do semi-supervised training of a hybrid model by providing some cluster assignments
Dotted glass I am trying to do automatic image segmentation of different regions of a 2D MR image based on pixel intensity values. The first step is to implement a Gaussian mixture model on the histogram of the image. I need to plot the resulting Gaussian obta
Avpenn I have images that I want to subdivide using a Gaussian mixture model scikit-learn. Some images have labels, so I want to use a lot of prior information. I would like to do semi-supervised training of a hybrid model by providing some cluster assignments
Benjamin Doughty I am interested in fitting a 2-component Gaussian mixture model to the data shown below. However, since I'm plotting log-transformed counts here, normalized to be between 0-1, the maximum value my data will take is 0. When I try to do a naive
Newkid I want to perform cross validation on my Gaussian mixture model. Currently, my cross_validationapproach using sklearn is as follows. clf = GaussianMixture(n_components=len(np.unique(y)), covariance_type='full')
cv_ortho = cross_validate(clf, parameters_
Book I've been using Scikit-learn's GMM function. First, I created a distribution along the line x=y. from sklearn import mixture
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
line_model = mixture.GMM(n_components
BenB I've been using Scikit-learn's GMM function. First, I created a distribution along the line x=y. from sklearn import mixture
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
line_model = mixture.GMM(n_components
Ulf Aslak: I have some 2D data (GPS data) with clusters (stop locations) that I know are similar to Gaussians with characteristic standard deviations (proportional to the inherent noise of GPS samples). The image below shows a sample, I would like it to have t
Benjamin Doughty I am interested in fitting a 2-component Gaussian mixture model to the data shown below. However, since I'm plotting log-transformed counts here, normalized to be between 0-1, the maximum value my data will take is 0. When I try to do a naive
golden_truth I have D-dimensional data with K components. How many parameters do I need if I use a model with a full covariance matrix? and if I use the diagonal covariance matrix how many? golden_truth xyLe_ 's answer in CrossValidated https://stats.stackexch
Newkid I want to perform cross validation on my Gaussian mixture model. Currently, my cross_validationapproach using sklearn is as follows. clf = GaussianMixture(n_components=len(np.unique(y)), covariance_type='full')
cv_ortho = cross_validate(clf, parameters_
Dotted glass I am trying to do automatic image segmentation of different regions of a 2D MR image based on pixel intensity values. The first step is to implement a Gaussian mixture model on the histogram of the image. I need to plot the resulting Gaussian obta
Dotted glass I am trying to do automatic image segmentation of different regions of a 2D MR image based on pixel intensity values. The first step is to implement a Gaussian mixture model on the histogram of the image. I need to plot the resulting Gaussian obta
Benjamin Doughty I am interested in fitting a 2-component Gaussian mixture model to the data shown below. However, since I'm plotting log-transformed counts here, normalized to be between 0-1, the maximum value my data will take is 0. When I try to do a naive
Benjamin Doughty I am interested in fitting a 2-component Gaussian mixture model to the data shown below. However, since I'm plotting log-transformed counts here, normalized to be between 0-1, the maximum value my data will take is 0. When I try to do a naive
Newkid I want to perform cross validation on my Gaussian mixture model. Currently, my cross_validationapproach using sklearn is as follows. clf = GaussianMixture(n_components=len(np.unique(y)), covariance_type='full')
cv_ortho = cross_validate(clf, parameters_
BenB I've been using Scikit-learn's GMM function. First, I created a distribution along the line x=y. from sklearn import mixture
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
line_model = mixture.GMM(n_components