The CNN_2D class implements a Convolutionsl Network model. The model is trained using a data matrix x and response matrix y.
Value parameters
- f
-
the activation function family for layers 1->2 (input to hidden)
- f1
-
the activation function family for layers 2->3 (hidden to output)
- fname_
-
the feature/variable names (defaults to null)
- hparam
-
the hyper-parameters for the model/network
- itran
-
the inverse transformation function returns responses to original scale
- nc
-
the width of the filters (size of cofilters)
- nf
-
the number of filters for this convolutional layer
- x
-
the input/data matrix with instances stored in rows
- y
-
the output/response matrix, where y_i = response for row i of matrix x
Attributes
- Companion
- object
- Graph
-
- Supertypes
Members list
Value members
Concrete methods
Build a sub-model that is restricted to the given columns of the data matrix.
Build a sub-model that is restricted to the given columns of the data matrix.
Value parameters
- x_cols
-
the columns that the new model is restricted to
Attributes
As seen from class CNN_2D, the missing signatures are as follows. For convenience, these are usable as stub implementations. FIX - put in new trait
As seen from class CNN_2D, the missing signatures are as follows. For convenience, these are usable as stub implementations. FIX - put in new trait
Attributes
Filter the i-th input vector with the f-th filter.
Filter the i-th input vector with the f-th filter.
Value parameters
- f
-
the index of the f-th filter
- i
-
the index of the i-th row of the matrix
Attributes
Return the feature/variable names.
Return the feature/variable names.
Attributes
Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_, e.g., SymbolicRegression.quadratic adds squared columns.
Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_, e.g., SymbolicRegression.quadratic adds squared columns.
Attributes
Return the used response vector y. Mainly for derived classes where y is transformed, e.g., TranRegression, ARX.
Return the used response vector y. Mainly for derived classes where y is transformed, e.g., TranRegression, ARX.
Attributes
Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.
Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.
Attributes
Return the vector of model parameter/coefficient values. Single output models have VectorD parameters, while multi-output models have MatrixD.
Return the vector of model parameter/coefficient values. Single output models have VectorD parameters, while multi-output models have MatrixD.
Attributes
Return the parameters c and b.
Return the parameters c and b.
Attributes
Predict the value of y = f(z) by evaluating the model equation. Single output models return Double, while multi-output models return VectorD.
Predict the value of y = f(z) by evaluating the model equation. Single output models return Double, while multi-output models return VectorD.
Value parameters
- z
-
the new vector to predict
Attributes
Given a new input vector z, predict the output/response vector f(z).
Given a new input vector z, predict the output/response vector f(z).
Value parameters
- z
-
the new input vector
Attributes
Given an input matrix z, predict the output/response matrix f(z).
Given an input matrix z, predict the output/response matrix f(z).
Value parameters
- z
-
the input matrix
Attributes
Test/evaluate the model's Quality of Fit (QoF) and return the predictions and QoF vectors. This may include the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classess should implement various diagnostics for the test and full (training + test) datasets.
Test/evaluate the model's Quality of Fit (QoF) and return the predictions and QoF vectors. This may include the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classess should implement various diagnostics for the test and full (training + test) datasets.
Value parameters
- x_
-
the testiing/full data/input matrix (impl. classes may default to x)
- y_
-
the testiing/full response/output vector (impl. classes may default to y)
Attributes
Test a predictive model y_ = f(x_) + e and return its QoF vector. Testing may be be in-sample (on the training set) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train before test.
Test a predictive model y_ = f(x_) + e and return its QoF vector. Testing may be be in-sample (on the training set) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train before test.
Value parameters
- x_
-
the testing/full data/input matrix (defaults to full x)
- y_
-
the testing/full response/output matrix (defaults to full y)
Attributes
Train the model 'y_ = f(x_) + e' on a given dataset, by optimizing the model parameters in order to minimize error '||e||' or maximize log-likelihood 'll'.
Train the model 'y_ = f(x_) + e' on a given dataset, by optimizing the model parameters in order to minimize error '||e||' or maximize log-likelihood 'll'.
Value parameters
- x_
-
the training/full data/input matrix (impl. classes may default to x)
- y_
-
the training/full response/output vector (impl. classes may default to y)
Attributes
Given training data x_ and y_, fit the parametera c and b. This is a simple algorithm that iterates over several epochs using gradient descent. It does not use batching nor a sufficient stopping rule. In practice, use the train2 method that uses a better optimizer.
Given training data x_ and y_, fit the parametera c and b. This is a simple algorithm that iterates over several epochs using gradient descent. It does not use batching nor a sufficient stopping rule. In practice, use the train2 method that uses a better optimizer.
Value parameters
- x_
-
the training/full data/input matrix
- y_
-
the training/full response/output matrix
Attributes
Given training data x_ and y_, fit the parameters c and b. Iterate over several epochs, where each epoch divides the training set into batches. Each batch is used to update the weights. FIX - to be implemented
Given training data x_ and y_, fit the parameters c and b. Iterate over several epochs, where each epoch divides the training set into batches. Each batch is used to update the weights. FIX - to be implemented
Value parameters
- x_
-
the training/full data/input matrix
- y_
-
the training/full response/output matrix
Attributes
Update filter f's parameters.
Update filter f's parameters.
Value parameters
- f
-
the index for the filter
- mat2
-
the new paramters for the filter's vector
Attributes
Inherited methods
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.
Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.
Value parameters
- w
-
the weights on the instances (defaults to null)
- y_
-
the actual response/output vector to use (test/full)
- yp_
-
the predicted response/output vector (test/full)
Attributes
- See also
-
Regression_WLS - Definition Classes
- Inherited from:
- Fit
Diagnose the health of the model by computing the Quality of Fit (QoF) metrics/measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. Include interval measures. Note: wis should be computed separately.
Diagnose the health of the model by computing the Quality of Fit (QoF) metrics/measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted. Include interval measures. Note: wis should be computed separately.
Value parameters
- alpha
-
the nominal level of uncertainty (alpha) (defaults to 0.9, 90%)
- low
-
the predicted lower bound
- up
-
the predicted upper bound
- w
-
the weights on the instances (defaults to null)
- y
-
the actual response/output vector to use (test/full)
- yp
-
the point prediction mean/median
Attributes
- See also
-
Regression_WLS - Inherited from:
- Fit
Diagnose the health of the model by computing the Quality of Fit (QoF) measures,
Diagnose the health of the model by computing the Quality of Fit (QoF) measures,
Value parameters
- alphas
-
the array of prediction levels
- low
-
the lower bounds for various alpha levels
- up
-
the upper bounds for various alpha levels
- y
-
the given time-series (must be aligned with the interval forecast)
- yp
-
the point prediction mean/median
Attributes
- Inherited from:
- Fit
Return the Quality of Fit (QoF) measures corresponding to the labels given. Note, if sse > sst, the model introduces errors and the rSq may be negative, otherwise, R^2 (rSq) ranges from 0 (weak) to 1 (strong). Override to add more quality of fit measures.
Return the Quality of Fit (QoF) measures corresponding to the labels given. Note, if sse > sst, the model introduces errors and the rSq may be negative, otherwise, R^2 (rSq) ranges from 0 (weak) to 1 (strong). Override to add more quality of fit measures.
Attributes
- Definition Classes
- Inherited from:
- Fit
Return the the y-transformation.
Return the used response matrix y, if needed.
Return the used response matrix y, if needed.
Attributes
- See also
-
neuralnet.PredictorMV - Inherited from:
- Model
Return the help string that describes the Quality of Fit (QoF) measures provided by the Fit trait. Override to correspond to fitLabel.
The log-likelihood function times -2. Override as needed.
The log-likelihood function times -2. Override as needed.
Value parameters
- ms
-
raw Mean Squared Error
- s2
-
MLE estimate of the population variance of the residuals
Attributes
- See also
- Inherited from:
- Fit
Return the mean of the squares for error (sse / df). Must call diagnose first.
Return the mean of the squares for error (sse / df). Must call diagnose first.
Attributes
- Inherited from:
- Fit
Return the coefficient of determination (R^2). Must call diagnose first.
Return the coefficient of determination (R^2). Must call diagnose first.
Attributes
- Inherited from:
- FitM
Return a basic report on a trained and tested multi-variate model.
Return a basic report on a trained and tested multi-variate model.
Value parameters
- ftMat
-
the matrix of qof values produced by the
Fittrait
Attributes
- Inherited from:
- Model
Return a basic report on a trained and tested model.
Return a basic report on a trained and tested model.
Value parameters
- ftVec
-
the vector of qof values produced by the
Fittrait
Attributes
- Inherited from:
- Model
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.
Value parameters
- df_update
-
the updated degrees of freedom (model, error)
Attributes
- Inherited from:
- Fit
Show the prediction interval forecasts and relevant QoF metrics/measures.
Show the prediction interval forecasts and relevant QoF metrics/measures.
Value parameters
- h
-
the forecasting horizon
- low
-
the predicted lower bound
- qof_all
-
all the QoF metrics (for point and interval forecasts)
- up
-
the predicted upper bound
- yfh
-
the forecasts for horizon h
- yy
-
the aligned actual response/output vector to use (test/full)
Attributes
- Inherited from:
- Fit
Return the symmetric Mean Absolute Percentage Error (sMAPE) score. Caveat: y_i = yp_i = 0 => no error => no percentage error
Return the symmetric Mean Absolute Percentage Error (sMAPE) score. Caveat: y_i = yp_i = 0 => no error => no percentage error
Value parameters
- e_
-
the error/residual vector (if null, recompute)
- y
-
the given time-series (must be aligned with the forecast)
- yp
-
the forecasted time-series
Attributes
- Inherited from:
- FitM
Return the sum of the squares for error (sse). Must call diagnose first.
Return the sum of the squares for error (sse). Must call diagnose first.
Attributes
- Inherited from:
- FitM
Produce a QoF summary for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). Note: `Fac_Cholesky is used to compute the inverse of xtx.
Produce a QoF summary for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). Note: `Fac_Cholesky is used to compute the inverse of xtx.
Value parameters
- b
-
the parameters/coefficients for the model
- fname
-
the array of feature/variable names
- vifs
-
the Variance Inflation Factors (VIFs)
- x_
-
the testing/full data/input matrix
Attributes
- Definition Classes
- Inherited from:
- Fit
Concrete fields
Inherited fields
The optional reference to an ontological concept