# get_prob: Computes model class conditional probabilities In IshmaelBelghazi/gpuClassifieR: gpuClassifieR C/CUDA linear classifier for R

## Description

Computes model class conditional probabilities

## Usage

 1 2 3 4 5 6 get_prob(object, feats, normalize = TRUE, log_domain = FALSE, backend = "R", ...) ## S3 method for class 'model.spec' get_prob(object, feats, normalize = TRUE, log_domain = FALSE, backend = "R", ...) 

## Arguments

 object Linear classifier specification object. feats Numeric Matrix of features. Follows the usual convention of having one example per row. For a model with M features and dataset with N examples the matrix should be N \times K normalize Logical scalar. If TRUE return normalized probabilities log_domain Logical scalar. If TRUE returns log probabilities. backend Computation back-end ('R', 'C', or 'CUDA') ... other arguments passed to specific methods

## Value

Numeric Matrix. Class conditional probabilities matrix. Each row corresponds to the probabilities of one example. For model with K classes and a dataset of N examples the returned matrix should be N \times K

## Methods (by class)

• model.spec: Computes model class conditional probabilities for linear classifier specification objects

## Author(s)

Mohamed Ishmael Diwan Belghazi

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 # Generate random initial weights w_init <- matrix(rnorm(784 * 10), 784, 10) # construct model linear_classifier <- Classifier(weights_init=w_init) # Fetch training variables feats <- mini_mnist$train$images targets <- mini_mnist$train$labels # Set decay coefficient decay <- 0.01 # compute log probabilities of the training set using the three back-ends log_prob_R <- get_prob(linear_classifier, feats, TRUE, TRUE, 'R') log_prob_C <- get_prob(linear_classifier, feats, TRUE, TRUE, 'C') log_prob_CUDA <- get_prob(linear_classifier, feats, TRUE, TRUE, 'CUDA') 

IshmaelBelghazi/gpuClassifieR documentation built on May 7, 2019, 6:45 a.m.