¡@

Home 

python Programming Glossary: dimensionality

Writing Python bindings for C++ code that use OpenCV

http://stackoverflow.com/questions/12957492/writing-python-bindings-for-c-code-that-use-opencv

int ndims PyArray_NDIM o if ndims CV_MAX_DIM failmsg s dimensionality d is too high name ndims return false int size CV_MAX_DIM 1.. int ndims PyArray_NDIM o if ndims CV_MAX_DIM failmsg s dimensionality d is too high name ndims return false int size CV_MAX_DIM 1..

testing whether a Numpy array contains a given row

http://stackoverflow.com/questions/14766194/testing-whether-a-numpy-array-contains-a-given-row

.any which makes sense for all combinations of a and b dimensionality ... EDIT Just to be clear this is not necessarily the expected..

Principal component analysis in Python

http://stackoverflow.com/questions/1730600/principal-component-analysis-in-python

I'd like to use principal component analysis PCA for dimensionality reduction. Does numpy or scipy already have it or do I have..

Dimension Reduction in Categorical Data with missing values

http://stackoverflow.com/questions/2837850/dimension-reduction-in-categorical-data-with-missing-values

to deal with ordinal and nominal variables. As for dimensionality reduction for categorical data i.e. a way to arrange variables..

How to sort my paws?

http://stackoverflow.com/questions/4502656/how-to-sort-my-paws

do this we need some sort of paw metric that is the same dimensionality for any dog. In the full dataset there are both very large and.. is a common problem in image recognition. Due to the high dimensionality of the input data and the somewhat fuzzy nature of images i.e... analysis and basically provides a way to reduce the dimensionality of our data so that distance is a good measure of shape. Because..

Calculating the percentage of variance measure for k-means?

http://stackoverflow.com/questions/6645895/calculating-the-percentage-of-variance-measure-for-k-means

Principal Components Analysis is applied to reduce the dimensionality from 64 down to 2 import numpy as np from scipy.cluster.vq import.. data datasets.load_digits t data 'target' # perform PCA dimensionality reduction pca RandomizedPCA n_components 2 .fit data 'data'..