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use simd_aligned::{MatrixD, Rows, VectorD};
use std::convert::TryFrom;
use crate::{
errors::Error,
f32s, f64s,
parser::ModelFile,
svm::{
class::Class,
kernel::{KernelDense, Linear, Poly, Rbf, Sigmoid},
predict::Predict,
problem::{Problem, Solution},
Probabilities, SVMType,
},
util::{find_max_index, set_all, sigmoid_predict},
vectors::Triangular,
};
/// A SVM using [SIMD](https://en.wikipedia.org/wiki/SIMD) intrinsics optimized for speed.
///
///
/// # Creating a SVM
///
/// This SVM can be created by passing a [`ModelFile`](crate::ModelFile) into `try_from`, or a `&str`:
///
/// ```
/// use ffsvm::*;
/// use std::convert::TryFrom;
///
/// let svm = DenseSVM::try_from("...");
/// ```
pub struct DenseSVM {
/// Total number of support vectors
pub(crate) num_total_sv: usize,
/// Number of attributes per support vector
pub(crate) num_attributes: usize,
pub(crate) rho: Triangular<f64>,
pub(crate) probabilities: Option<Probabilities>,
pub(crate) svm_type: SVMType,
/// SVM specific data needed for classification
pub(crate) kernel: Box<dyn KernelDense>,
/// All classes
pub(crate) classes: Vec<Class<MatrixD<f32s, Rows>>>,
}
impl DenseSVM {
/// Finds the class index for a given label.
///
/// # Description
///
/// This method takes a `label` as defined in the libSVM training model
/// and returns the internal `index` where this label resides. The index
/// equals [`Problem::probabilities`] index where that label's
/// probability can be found.
///
/// # Returns
///
/// If the label was found its index returned in the [`Option`]. Otherwise `None`
/// is returned.
pub fn class_index_for_label(&self, label: i32) -> Option<usize> {
for (i, class) in self.classes.iter().enumerate() {
if class.label != label {
continue;
}
return Some(i);
}
None
}
/// Returns the class label for a given index.
///
/// # Description
///
/// The inverse of [`DenseSVM::class_index_for_label`], this function returns the class label
/// associated with a certain internal index. The index equals the [`Problem::probabilities`]
/// index where a label's probability can be found.
///
/// # Returns
///
/// If the index was found it is returned in the [`Option`]. Otherwise `None`
/// is returned.
pub fn class_label_for_index(&self, index: usize) -> Option<i32> {
if index >= self.classes.len() {
None
} else {
Some(self.classes[index].label)
}
}
/// Computes the kernel values for this problem
pub(crate) fn compute_kernel_values(&self, problem: &mut Problem<VectorD<f32s>>) {
// Get current problem and decision values array
let features = &problem.features;
let kernel_values = &mut problem.kernel_values;
// Compute kernel values per class
for (i, class) in self.classes.iter().enumerate() {
let kvalues = kernel_values.row_as_flat_mut(i);
self.kernel.compute(&class.support_vectors, features.as_raw(), kvalues);
}
}
// This is pretty much copy-paste of `multiclass_probability` from libSVM which we need
// to be compatibly for predicting probability for multiclass SVMs. The method is in turn
// based on Method 2 from the paper "Probability Estimates for Multi-class
// Classification by Pairwise Coupling", Journal of Machine Learning Research 5 (2004) 975-1005,
// by Ting-Fan Wu, Chih-Jen Lin and Ruby C. Weng.
pub(crate) fn compute_multiclass_probabilities(&self, problem: &mut Problem<VectorD<f32s>>) -> Result<(), Error> { compute_multiclass_probabilities_impl!(self, problem) }
/// Based on kernel values, computes the decision values for this problem.
pub(crate) fn compute_classification_values(&self, problem: &mut Problem<VectorD<f32s>>) { compute_classification_values_impl!(self, problem) }
/// Based on kernel values, computes the decision values for this problem.
pub(crate) fn compute_regression_values(&self, problem: &mut Problem<VectorD<f32s>>) {
use simd_aligned::SimdExt;
let class = &self.classes[0];
let coef = class.coefficients.row(0);
let kvalues = problem.kernel_values.row(0);
let mut sum = coef.iter().zip(kvalues).map(|(a, b)| (*a * *b).sum()).sum::<f64>();
sum -= self.rho[0];
problem.result = Solution::Value(sum as f32);
}
/// Returns number of attributes, reflecting the libSVM model.
pub const fn attributes(&self) -> usize { self.num_attributes }
/// Returns number of classes, reflecting the libSVM model.
pub fn classes(&self) -> usize { self.classes.len() }
}
impl Predict<VectorD<f32s>, VectorD<f64s>> for DenseSVM {
fn predict_probability(&self, problem: &mut Problem<VectorD<f32s>>) -> Result<(), Error> { predict_probability_impl!(self, problem) }
// Predict the value for one problem.
fn predict_value(&self, problem: &mut Problem<VectorD<f32s>>) -> Result<(), Error> {
match self.svm_type {
SVMType::CSvc | SVMType::NuSvc => {
// Compute kernel, decision values and eventually the label
self.compute_kernel_values(problem);
self.compute_classification_values(problem);
// Compute highest vote
let highest_vote = find_max_index(&problem.vote);
problem.result = Solution::Label(self.classes[highest_vote].label);
Ok(())
}
SVMType::ESvr | SVMType::NuSvr => {
self.compute_kernel_values(problem);
self.compute_regression_values(problem);
Ok(())
}
}
}
}
impl<'a, 'b> TryFrom<&'a str> for DenseSVM {
type Error = Error;
fn try_from(input: &'a str) -> Result<Self, Error> {
let raw_model = ModelFile::try_from(input)?;
Self::try_from(&raw_model)
}
}
impl<'a, 'b> TryFrom<&'a ModelFile<'b>> for DenseSVM {
type Error = Error;
fn try_from(raw_model: &'a ModelFile<'_>) -> Result<Self, Error> {
let (mut svm, nr_sv) = prepare_svm!(raw_model, dyn KernelDense, MatrixD<f32s, Rows>, Self);
let vectors = &raw_model.vectors;
// Things down here are a bit ugly as the file format is a bit ugly ...
// Now read all vectors and decode stored information
let mut start_offset = 0;
// In the raw file, support vectors are grouped by class
for (i, num_sv_per_class) in nr_sv.iter().enumerate() {
let stop_offset = start_offset + *num_sv_per_class as usize;
// Set support vector and coefficients
for (i_vector, vector) in vectors[start_offset .. stop_offset].iter().enumerate() {
let mut last_attribute = None;
// Set support vectors
for (i_attribute, attribute) in vector.features.iter().enumerate() {
if let Some(last) = last_attribute {
// In case we have seen an attribute already, this one must be strictly
// the successor attribute
if attribute.index != last + 1 {
return Result::Err(Error::AttributesUnordered {
index: attribute.index,
value: attribute.value,
last_index: last,
});
}
};
let mut support_vectors = svm.classes[i].support_vectors.flat_mut();
support_vectors[(i_vector, i_attribute)] = attribute.value;
last_attribute = Some(attribute.index);
}
// Set coefficients
for (i_coefficient, coefficient) in vector.coefs.iter().enumerate() {
let mut coefficients = svm.classes[i].coefficients.flat_mut();
coefficients[(i_coefficient, i_vector)] = f64::from(*coefficient);
}
}
// Update last offset.
start_offset = stop_offset;
}
// Return what we have
Result::Ok(svm)
}
}
#[cfg(test)]
mod tests {
use crate::*;
use std::convert::TryFrom;
#[test]
fn class_operations() -> Result<(), Error> {
let svm = DenseSVM::try_from(SAMPLE_MODEL)?;
assert_eq!(None, svm.class_index_for_label(0));
assert_eq!(Some(1), svm.class_index_for_label(42));
Ok(())
}
}