Updated docs [skip ci]
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@@ -90,10 +90,10 @@ libmf::Model::params()
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.threads(12) // number of threads
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.bins(25) // number of bins
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.iterations(20) // number of iterations
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.lambda_p1(0.0) // coefficient of L1-norm regularization on P
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.lambda_p2(0.1) // coefficient of L2-norm regularization on P
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.lambda_q1(0.0) // coefficient of L1-norm regularization on Q
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.lambda_q2(0.1) // coefficient of L2-norm regularization on Q
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.lambda_p1(0.0) // coefficient of L1-norm regularization for P
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.lambda_p2(0.1) // coefficient of L2-norm regularization for P
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.lambda_q1(0.0) // coefficient of L1-norm regularization for Q
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.lambda_q2(0.1) // coefficient of L2-norm regularization for Q
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.learning_rate(0.1) // learning rate
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.alpha(1.0) // importance of negative entries
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.c(0.0001) // desired value of negative entries
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@@ -43,25 +43,25 @@ impl Params {
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self
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}
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/// Sets the coefficient of L1-norm regularization on P.
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/// Sets the coefficient of L1-norm regularization for P.
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pub fn lambda_p1(&mut self, value: f32) -> &mut Self {
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self.param.lambda_p1 = value;
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self
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}
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/// Sets the coefficient of L2-norm regularization on P.
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/// Sets the coefficient of L2-norm regularization for P.
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pub fn lambda_p2(&mut self, value: f32) -> &mut Self {
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self.param.lambda_p2 = value;
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self
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}
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/// Sets the coefficient of L1-norm regularization on Q.
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/// Sets the coefficient of L1-norm regularization for Q.
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pub fn lambda_q1(&mut self, value: f32) -> &mut Self {
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self.param.lambda_q1 = value;
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self
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}
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/// Sets the coefficient of L2-norm regularization on Q.
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/// Sets the coefficient of L2-norm regularization for Q.
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pub fn lambda_q2(&mut self, value: f32) -> &mut Self {
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self.param.lambda_q2 = value;
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self
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