Man pages for tbonza/supml
"Algorithms for Supervised Machine Learning"

analyticlm	Analytic solution for least squares
analyticridge	Functions associated with ridge regression Analytic solution...
argmax	Given a matrix, find the max row value position
bayes	Bayesian object
bernoulli_sim	Simulate Bernoulli distribution from HW3
bgd	Gradient descent algorithms
binomcdf	Compute the binomial cumulative density function
binom_mean	Mean of binomial variable
binompdf	Compute the binomial probability density function
binom_sd	Standard deviation of binomial variable
categoricalProbs.bayes	Conditional probability fitting step for Categorical data
combs	Discrete random variables - binomial probability Compute the...
confusionMatrix.bayes	Confusion matrix for reporting accuracy
continuousProbs.bayes	Conditional probability fitting step for Continuous data
coord_cut	Utility functions for working with Bayesian classifier Create...
create_outliers	Create outliers for 1-d vector X from hw1 specs
create_splits	For use with in generating training and test sets Create...
data_simulator	Simulate data for hw
fit.bayes	Fit training data to Bayesian Classifier
gauss_kern	Kernels used for localized estimation Gaussian Kernel
geometcdf	Geometric cumulative density function
geometpdf	Geometric probability density function for first success in x...
gridSearch.bayes	Implement grid search for the bayes object
grp_covar	Return a p*p covariance matrix for class K
grp_indices	Implementations related to Discriminant analysis Create...
grp_mean	Mean of each predictor attribute for each class K
huber_cond	Conditional for Huber Loss function
huber_loss	Huber Loss function (smooth mean absolute error) with...
kblock_kern	K-Block Kernel
kernelDensity.bayes	Kernel Density Estimate for Naive Bayes
lasso_gradient	Functions associated with lasso regression
ldf	Linear discriminant function (LDF) implementation
least_squares	Least squares objective
least_squares_gradient	Least squares gradient
least_squares_huber_gradient	Least Squares with Huber Loss Function gradient
least_squares_l1_gradient	Least Squares with Mean Absolute Error (L1 norm) gradient
least_squares_ql_gradient	Least Squares with Quadratic Loss gradient
lgm_gradient	Logistic gradient computation
lgm_yhat	Implementations for Logistic Regression Compute yhat for...
linda	Linear Discriminant Analysis implementation
local_lm	Local Linear Regression
logistic_gradient	Compute the logistic gradient with learning rate alpha
LOGMIN	Implement Naive Bayes Classifier
lsg	Least squares gradient with different handling of alpha
mae	Compute Mean Absolute Error (L1 norm)
matrix_group	Convert matrix to visualization format
mtcars	Testing data documentation mtcars dataset
normalize	Normalize a numeric object
nsim	Simulate a random normal distribution on an interval
pi_k	Compute prior probabilities for each class K
plotgrid	Coordinate grid for plotting
pooled_covar	Compute Pooled Covariance Matrix
popmat	Populate a Spatial Matrix with Aggregated Values
postSpatialProbs.bayes	Posterior Spatial probabilties
predict.bayes	Predict log probabilities for each class K
predict_proba.bayes	Predicts the log probabilities for each class and feature
priorProbs.bayes	Prior probability fitting step for Continous/Categorical data
quadratic_loss	Loss functions from hw1 - related to linear regression...
score.bayes	Accuracy scoring function for Binomial Naive Bayes
sgd	Stochastic Gradient Descent
spatial_fit.bayes	Fit spatial data
spatialProbs.bayes	Conditional probability fitting step for Spatial data
w	mtcars derivative variables
wls	Implement local linear regression with a Gaussian kernel, as...
x	mtcars derivative variables
y	mtcars derivative variables