Merge branch 'master' of https://github.com/ddbourgin/numpy-ml into master

Author: ddbourgin

numpy_ml/README.md

@@ -92,6 +92,8 @@ This repo includes code for the following models:
  - Ridge regression
  - Logistic regression
  - Ordinary least squares
+ - Gaussian naive Bayes classifier
+ - Generalized linear model (identity, log, and logit links)
  - Bayesian linear regression w/ conjugate priors
  - Unknown mean, known variance (Gaussian prior)
  - Unknown mean, unknown variance (Normal-Gamma / Normal-Inverse-Wishart prior)

numpy_ml/linear_models/README.md

@@ -1,14 +1,17 @@
 # Linear Models
-The `lm.py` module implements:
+The `linear_models` module includes:
 
-1. [OLS linear regression](https://en.wikipedia.org/wiki/Ordinary_least_squares) with maximum likelihood parameter estimates via the normal equation. For both (Online and Batch mode)
-2. [Ridge regression / Tikhonov regularization](https://en.wikipedia.org/wiki/Tikhonov_regularization)
+1. [OLS linear regression](https://en.wikipedia.org/wiki/Ordinary_least_squares) with maximum likelihood parameter estimates via the normal equation. 
+ - Includes optional weight arguments for [weighted least squares](https://en.wikipedia.org/wiki/Weighted_least_squares)
+ - Supports batch and online coefficient updates.
+3. [Ridge regression / Tikhonov regularization](https://en.wikipedia.org/wiki/Tikhonov_regularization)
  with maximum likelihood parameter estimates via the normal equation.
 2. [Logistic regression](https://en.wikipedia.org/wiki/Logistic_regression) with maximum likelihood parameter estimates via gradient descent.
 3. [Bayesian linear regression](https://en.wikipedia.org/wiki/Bayesian_linear_regression) with maximum a posteriori parameter estimates via [conjugacy](https://en.wikipedia.org/wiki/Conjugate_prior#Table_of_conjugate_distributions)
  - Known coefficient prior mean and known error variance
  - Known coefficient prior mean and unknown error variance
 4. [Naive Bayes classifier](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) with Gaussian feature likelihoods.
+5. [Generalized linear model](https://en.wikipedia.org/wiki/Generalized_linear_model) with identity, log, and logit link functions.
 
 ## Plots
 <p align="center">