mlpack
2.2.5
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Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values. More...
Public Member Functions | |
SoftmaxRegression (const size_t inputSize, const size_t numClasses, const bool fitIntercept=false) | |
Initialize the SoftmaxRegression without performing training. More... | |
SoftmaxRegression (const arma::mat &data, const arma::Row< size_t > &labels, const size_t numClasses, const double lambda=0.0001, const bool fitIntercept=false) | |
Construct the SoftmaxRegression class with the provided data and labels. More... | |
SoftmaxRegression (OptimizerType< SoftmaxRegressionFunction > &optimizer) | |
Construct the softmax regression model with the given training data. More... | |
void | Classify (const arma::mat &dataset, arma::Row< size_t > &labels) const |
Classify the given points, returning the predicted labels for each point. More... | |
double | ComputeAccuracy (const arma::mat &testData, const arma::Row< size_t > &labels) const |
Computes accuracy of the learned model given the feature data and the labels associated with each data point. More... | |
size_t | FeatureSize () const |
Gets the features size of the training data. More... | |
bool | FitIntercept () const |
Gets the intercept term flag. We can't change this after training. More... | |
double & | Lambda () |
Sets the regularization parameter. More... | |
double | Lambda () const |
Gets the regularization parameter. More... | |
size_t & | NumClasses () |
Sets the number of classes. More... | |
size_t | NumClasses () const |
Gets the number of classes. More... | |
arma::mat & | Parameters () |
Get the model parameters. More... | |
const arma::mat & | Parameters () const |
Get the model parameters. More... | |
mlpack_deprecated void | Predict (const arma::mat &testData, arma::Row< size_t > &predictions) const |
Predict the class labels for the provided feature points. More... | |
template<typename Archive > | |
void | Serialize (Archive &ar, const unsigned int) |
Serialize the SoftmaxRegression model. More... | |
double | Train (OptimizerType< SoftmaxRegressionFunction > &optimizer) |
Train the softmax regression model with the given optimizer. More... | |
double | Train (const arma::mat &data, const arma::Row< size_t > &labels, const size_t numClasses) |
Train the softmax regression with the given training data. More... | |
Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values.
It is a generalization of Logistic Regression (which is used only for binary classification). The model has a different set of parameters for each class, but can be easily converted into a vectorized implementation as has been done in this module. The model can be used for direct classification of feature data or in conjunction with unsupervised learning methods. More technical details about the model can be found on the following webpage:
http://ufldl.stanford.edu/wiki/index.php/Softmax_Regression
An example on how to use the interface is shown below:
Definition at line 65 of file softmax_regression.hpp.
SoftmaxRegression | ( | const size_t | inputSize, |
const size_t | numClasses, | ||
const bool | fitIntercept = false |
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) |
Initialize the SoftmaxRegression without performing training.
Default value of lambda is 0.0001. Be sure to use Train() before calling Classify() or ComputeAccuracy(), otherwise the results may be meaningless.
inputSize | Size of the input feature vector. |
numClasses | Number of classes for classification. |
fitIntercept | add intercept term or not. |
SoftmaxRegression | ( | const arma::mat & | data, |
const arma::Row< size_t > & | labels, | ||
const size_t | numClasses, | ||
const double | lambda = 0.0001 , |
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const bool | fitIntercept = false |
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) |
Construct the SoftmaxRegression class with the provided data and labels.
This will train the model. Optionally, the parameter 'lambda' can be passed, which controls the amount of L2-regularization in the objective function. By default, the model takes a small value.
data | Input training features. Each column associate with one sample |
labels | Labels associated with the feature data. |
inputSize | Size of the input feature vector. |
numClasses | Number of classes for classification. |
lambda | L2-regularization constant. |
fitIntercept | add intercept term or not. |
SoftmaxRegression | ( | OptimizerType< SoftmaxRegressionFunction > & | optimizer | ) |
Construct the softmax regression model with the given training data.
This will train the model. This overload takes an already instantiated optimizer and uses it to train the model. The optimizer should hold an instantiated SoftmaxRegressionFunction object for the function to operate upon. This option should be preferred when the optimizer options are to be changed.
optimizer | Instantiated optimizer with instantiated error function. |
void Classify | ( | const arma::mat & | dataset, |
arma::Row< size_t > & | labels | ||
) | const |
Classify the given points, returning the predicted labels for each point.
The function calculates the probabilities for every class, given a data point. It then chooses the class which has the highest probability among all.
dataset | Set of points to classify. |
labels | Predicted labels for each point. |
double ComputeAccuracy | ( | const arma::mat & | testData, |
const arma::Row< size_t > & | labels | ||
) | const |
Computes accuracy of the learned model given the feature data and the labels associated with each data point.
Predictions are made using the provided data and are compared with the actual labels.
testData | Matrix of data points using which predictions are made. |
labels | Vector of labels associated with the data. |
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Gets the features size of the training data.
Definition at line 186 of file softmax_regression.hpp.
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Gets the intercept term flag. We can't change this after training.
Definition at line 178 of file softmax_regression.hpp.
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Sets the regularization parameter.
Definition at line 173 of file softmax_regression.hpp.
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Gets the regularization parameter.
Definition at line 175 of file softmax_regression.hpp.
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Sets the number of classes.
Definition at line 168 of file softmax_regression.hpp.
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Gets the number of classes.
Definition at line 170 of file softmax_regression.hpp.
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Get the model parameters.
Definition at line 181 of file softmax_regression.hpp.
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Get the model parameters.
Definition at line 183 of file softmax_regression.hpp.
mlpack_deprecated void Predict | ( | const arma::mat & | testData, |
arma::Row< size_t > & | predictions | ||
) | const |
Predict the class labels for the provided feature points.
The function calculates the probabilities for every class, given a data point. It then chooses the class which has the highest probability among all.
This method is deprecated and will be removed in mlpack 3.0.0. You should use Classify() instead.
testData | Matrix of data points for which predictions are to be made. |
predictions | Vector to store the predictions in. |
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Serialize the SoftmaxRegression model.
Definition at line 194 of file softmax_regression.hpp.
References mlpack::data::CreateNVP().
double Train | ( | OptimizerType< SoftmaxRegressionFunction > & | optimizer | ) |
Train the softmax regression model with the given optimizer.
The optimizer should hold an instantiated SoftmaxRegressionFunction object for the function to operate upon. This option should be preferred when the optimizer options are to be changed.
optimizer | Instantiated optimizer with instantiated error function. |
double Train | ( | const arma::mat & | data, |
const arma::Row< size_t > & | labels, | ||
const size_t | numClasses | ||
) |
Train the softmax regression with the given training data.
data | Input data with each column as one example. |
labels | Labels associated with the feature data. |
numClasses | Number of classes for classification. |