class SVDReg extends Error
The SVDReg class works on the principle of Gradient Descent for minimizing
the error generated and L2 regularization, while predicting the missing value
in the matrix. This is obtained by the dot product of 'u(i)' and 'v(j)' vectors:
Dimensionality is reduced from 'n' features to 'k' factors.
predict (i, j) = u(i) dot v(j)
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- def calc_objf: Double
Calculate the value of the objective function after the 'u' and 'v' matrices are generated.
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- def factor: Unit
Factor the the input matrix 'a' to obtain the 'u' and the 'v' matrices.
- final def flaw(method: String, message: String): Unit
Show the flaw by printing the error message.
Show the flaw by printing the error message.
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the method where the error occurred
- message
the error message
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- def nz_sqmean: Double
Return the square root of the non-zero mean / k of the initial rating matrix.
- def predict(i: Int, j: Int): Double
Predict the value for given row and column.
Predict the value for given row and column.
- i
the row id
- j
the column id
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- def update(h: Int): Double
Update the 'u' and 'v' matrix to minimze sum of squared error and return the mean sum of squared errors.
Update the 'u' and 'v' matrix to minimze sum of squared error and return the mean sum of squared errors.
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the current column to update
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