Struct linfa_svm::Svm [−][src]
Expand description
Fitted Support Vector Machines model
This is the result of the SMO optimizer and contains the support vectors, quality of solution and optionally the linear hyperplane.
Fields
alpha: Vec<F>
rho: F
Implementations
Returns the number of support vectors
This function returns the number of support vectors which have an influence on the decision outcome greater than zero.
Sums the inner product of sample
and every one of the support vectors.
Parameters
sample
: the input sample
Returns
The sum of all inner products of sample
and every one of the support vectors, scaled by their weight.
Panics
If the shape of sample
is not compatible with the
shape of the support vectors
Trait Implementations
Display solution
In order to understand the solution of the SMO solver the objective, number of iterations and required support vectors are printed here.
Predict a probability with a feature vector
Predict a probability with a feature vector
Predict a probability with a feature vector
Predict a probability with a feature vector
Predict a probability with a feature vector
Predict a probability with a feature vector
Predict a probability with a feature vector Classify observations
This function takes a number of features and predicts target probabilities that they belong to the positive class.
Classify observations
This function takes a number of features and predicts target probabilities that they belong to the positive class.
Classify observations
This function takes a number of features and predicts target probabilities that they belong to the positive class.
Classify observations
This function takes a number of features and predicts target probabilities that they belong to the positive class.
Auto Trait Implementations
impl<F, T> RefUnwindSafe for Svm<F, T> where
F: RefUnwindSafe,
T: RefUnwindSafe,
impl<F, T> UnwindSafe for Svm<F, T> where
F: UnwindSafe + RefUnwindSafe,
T: UnwindSafe,
Blanket Implementations
Mutably borrows from an owned value. Read more