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| SoftmaxRegressionFunction (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 Softmax Regression objective function with the given parameters. More...
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double | Evaluate (const arma::mat ¶meters) const |
| Evaluates the objective function of the softmax regression model using the given parameters. More...
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size_t | FeatureSize () const |
| Gets the features size of the training data. More...
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bool | FitIntercept () const |
| Gets the intercept flag. More...
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void | GetGroundTruthMatrix (const arma::Row< size_t > &labels, arma::sp_mat &groundTruth) |
| Constructs the ground truth label matrix with the passed labels. More...
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const arma::mat & | GetInitialPoint () const |
| Return the initial point for the optimization. More...
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void | GetProbabilitiesMatrix (const arma::mat ¶meters, arma::mat &probabilities) const |
| Evaluate the probabilities matrix with the passed parameters. More...
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void | Gradient (const arma::mat ¶meters, arma::mat &gradient) const |
| Evaluates the gradient values of the objective function given the current set of parameters. More...
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const arma::mat | InitializeWeights () |
| Initializes the parameters of the model to suitable values. More...
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double & | Lambda () |
| Sets the regularization parameter. More...
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double | Lambda () const |
| Gets the regularization parameter. More...
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size_t | NumClasses () const |
| Gets the number of classes. More...
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static const arma::mat | InitializeWeights (const size_t featureSize, const size_t numClasses, const bool fitIntercept=false) |
| Initialize Softmax Regression weights (trainable parameters) with the given parameters. More...
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static void | InitializeWeights (arma::mat &weights, const size_t featureSize, const size_t numClasses, const bool fitIntercept=false) |
| Initialize Softmax Regression weights (trainable parameters) with the given parameters. More...
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SoftmaxRegressionFunction |
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const arma::mat & |
data, |
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const arma::Row< size_t > & |
labels, |
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const size_t |
numClasses, |
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const double |
lambda = 0.0001 , |
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const bool |
fitIntercept = false |
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Construct the Softmax Regression objective function with the given parameters.
- Parameters
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data | Input training data, 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 | Intercept term flag. |
double Evaluate |
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const arma::mat & |
parameters | ) |
const |
Evaluates the objective function of the softmax regression model using the given parameters.
The cost function has terms for the log likelihood error and the regularization cost. The objective function takes a low value when the model generalizes well for the given training data, while having small parameter values.
- Parameters
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parameters | Current values of the model parameters. |
size_t FeatureSize |
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const |
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bool FitIntercept |
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const |
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void GetGroundTruthMatrix |
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const arma::Row< size_t > & |
labels, |
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arma::sp_mat & |
groundTruth |
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Constructs the ground truth label matrix with the passed labels.
- Parameters
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labels | Labels associated with the training data. |
groundTruth | Pointer to arma::mat which stores the computed matrix. |
const arma::mat& GetInitialPoint |
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const |
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void GetProbabilitiesMatrix |
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const arma::mat & |
parameters, |
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arma::mat & |
probabilities |
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Evaluate the probabilities matrix with the passed parameters.
probabilities(i, j) = exp( * data_j) / sum_k(exp( * data_j)). It represents the probability of data_j belongs to class i.
- Parameters
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parameters | Current values of the model parameters. |
probabilities | Pointer to arma::mat which stores the probabilities. |
void Gradient |
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const arma::mat & |
parameters, |
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arma::mat & |
gradient |
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Evaluates the gradient values of the objective function given the current set of parameters.
The function calculates the probabilities for each class given the parameters, and computes the gradients based on the difference from the ground truth.
- Parameters
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parameters | Current values of the model parameters. |
gradient | Matrix where gradient values will be stored. |
const arma::mat InitializeWeights |
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Initializes the parameters of the model to suitable values.
static const arma::mat InitializeWeights |
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const size_t |
featureSize, |
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const size_t |
numClasses, |
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const bool |
fitIntercept = false |
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Initialize Softmax Regression weights (trainable parameters) with the given parameters.
- Parameters
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featureSize | The number of features in the training set. |
numClasses | Number of classes for classification. |
fitIntercept | If true, an intercept is fitted. |
- Returns
- Initialized model weights.
static void InitializeWeights |
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arma::mat & |
weights, |
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const size_t |
featureSize, |
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const size_t |
numClasses, |
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const bool |
fitIntercept = false |
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static |
Initialize Softmax Regression weights (trainable parameters) with the given parameters.
- Parameters
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weights | This will be filled with the initialized model weights. |
featureSize | The number of features in the training set. |
numClasses | Number of classes for classification. |
fitIntercept | Intercept term flag. |
size_t NumClasses |
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const |
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inline |
The documentation for this class was generated from the following file: