Statistical functions (mlpy.stats
)¶
Discrete distributions¶
nonuniform |
A non-uniform discrete random variable. |
gibbs |
A Gibbs distribution discrete random variable. |
Continuous random variables¶
random_floats |
Return random floats in the half-open interval [0.0, 1.0) between low and high, inclusive. |
Conditional distributions¶
conditional_normal |
Conditional Normal random variable. |
conditional_student |
Conditional Student random variable. |
conditional_mix_normal |
Conditional Mix-Normal random variable. |
Multivariate distributions¶
multivariate_normal |
Multivariate Normal random variable. |
multivariate_student |
Multivariate Student random variable. |
invwishart |
Inverse Wishart random variable. |
normal_invwishart |
Normal-Inverse Wishart random variable. |
Statistical Models¶
markov |
Markov model. |
Mixture Models¶
MixtureModel |
Mixture model base class. |
DiscreteMM |
Discrete mixture model class. |
GMM |
Gaussian mixture model class. |
StudentMM |
Student mixture model class. |
Statistical functions¶
is_posdef |
Test if matrix a is positive definite. |
randpd |
Create a random positive definite matrix. |
stacked_randpd |
Create multiple random positive definite matrices. |
normalize_logspace |
Normalize in log space while avoiding numerical underflow. |
sq_distance |
Efficiently compute squared Euclidean distances between stats of vectors. |
partitioned_cov |
Partition the rows of x according to y and take the covariance of each group. |
partitioned_mean |
Groups the rows of x according to the class labels in y and takes the mean of each group. |
partitioned_sum |
Groups the rows of x according to the class labels in y and sums each group. |
shrink_cov |
Ledoit-Wolf optimal shrinkage estimator. |
canonize_labels |
Transform labels to 1:k. |