work/smartcore
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Run: cargo clippy --fix -Z unstable-options and cargo fmt

Browse Source
This commit is contained in:
Luis Moreno
2020-11-08 19:39:11 -04:00
parent 8281a1620e
commit 860056c3ba
48 changed files with 367 additions and 395 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/svm/mod.rs
+6 -9
View File
@@ -48,7 +48,7 @@ impl Kernels {
/// Radial basis function kernel (Gaussian)
pub fn rbf<T: RealNumber>(gamma: T) -> RBFKernel<T> {
RBFKernel { gamma: gamma }
RBFKernel { gamma }
}
/// Polynomial kernel
@@ -57,9 +57,9 @@ impl Kernels {
/// * `coef0` - independent term in kernel function
pub fn polynomial<T: RealNumber>(degree: T, gamma: T, coef0: T) -> PolynomialKernel<T> {
PolynomialKernel {
degree: degree,
gamma: gamma,
coef0: coef0,
degree,
gamma,
coef0,
}
}
@@ -79,17 +79,14 @@ impl Kernels {
/// * `gamma` - kernel coefficient
/// * `coef0` - independent term in kernel function
pub fn sigmoid<T: RealNumber>(gamma: T, coef0: T) -> SigmoidKernel<T> {
SigmoidKernel {
gamma: gamma,
coef0: coef0,
}
SigmoidKernel { gamma, coef0 }
}
/// Sigmoid kernel
/// * `gamma` - kernel coefficient
pub fn sigmoid_with_gamma<T: RealNumber>(gamma: T) -> SigmoidKernel<T> {
SigmoidKernel {
gamma: gamma,
gamma,
coef0: T::one(),
}
}
src/svm/svc.rs
+31 -31
View File
@@ -173,9 +173,9 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SVC<T, M, K> {
let (n, _) = x.shape();
if n != y.len() {
return Err(Failed::fit(&format!(
"Number of rows of X doesn't match number of rows of Y"
)));
return Err(Failed::fit(
&"Number of rows of X doesn\'t match number of rows of Y".to_string(),
));
}
let classes = y.unique();
@@ -204,11 +204,11 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SVC<T, M, K> {
let (support_vectors, weight, b) = optimizer.optimize();
Ok(SVC {
classes: classes,
kernel: kernel,
classes,
kernel,
instances: support_vectors,
w: weight,
b: b,
b,
})
}
@@ -251,7 +251,7 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> PartialEq for SVC<
|| self.w.len() != other.w.len()
|| self.instances.len() != other.instances.len()
{
return false;
false
} else {
for i in 0..self.w.len() {
if (self.w[i] - other.w[i]).abs() > T::epsilon() {
@@ -263,7 +263,7 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> PartialEq for SVC<
return false;
}
}
return true;
true
}
}
}
@@ -278,12 +278,12 @@ impl<T: RealNumber, V: BaseVector<T>> SupportVector<T, V> {
};
SupportVector {
index: i,
x: x,
x,
grad: g,
k: k_v,
alpha: T::zero(),
cmin: cmin,
cmax: cmax,
cmin,
cmax,
}
}
}
@@ -291,7 +291,7 @@ impl<T: RealNumber, V: BaseVector<T>> SupportVector<T, V> {
impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Cache<'a, T, M, K> {
fn new(kernel: &'a K) -> Cache<'a, T, M, K> {
Cache {
kernel: kernel,
kernel,
data: HashMap::new(),
phantom: PhantomData,
}
@@ -326,8 +326,8 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
let (n, _) = x.shape();
Optimizer {
x: x,
y: y,
x,
y,
parameters: &parameters,
svmin: 0,
svmax: 0,
@@ -335,7 +335,7 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
gmax: T::min_value(),
tau: T::from_f64(1e-12).unwrap(),
sv: Vec::with_capacity(n),
kernel: kernel,
kernel,
recalculate_minmax_grad: true,
}
}
@@ -389,10 +389,11 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
if self.process(i, self.x.get_row(i), self.y.get(i), cache) {
cp += 1;
}
} else if self.y.get(i) == -T::one() && cn < few {
if self.process(i, self.x.get_row(i), self.y.get(i), cache) {
cn += 1;
}
} else if self.y.get(i) == -T::one()
&& cn < few
&& self.process(i, self.x.get_row(i), self.y.get(i), cache)
{
cn += 1;
}
if cp >= few && cn >= few {
@@ -420,10 +421,10 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
self.find_min_max_gradient();
if self.gmin < self.gmax {
if (y > T::zero() && g < self.gmin) || (y < T::zero() && g > self.gmax) {
return false;
}
if self.gmin < self.gmax
&& ((y > T::zero() && g < self.gmin) || (y < T::zero() && g > self.gmax))
{
return false;
}
for v in cache_values {
@@ -494,13 +495,12 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
let mut idxs_to_drop: HashSet<usize> = HashSet::new();
self.sv.retain(|v| {
if v.alpha == T::zero() {
if (v.grad >= gmax && T::zero() >= v.cmax)
|| (v.grad <= gmin && T::zero() <= v.cmin)
{
idxs_to_drop.insert(v.index);
return false;
}
if v.alpha == T::zero()
&& ((v.grad >= gmax && T::zero() >= v.cmax)
|| (v.grad <= gmin && T::zero() <= v.cmin))
{
idxs_to_drop.insert(v.index);
return false;
};
true
});
@@ -647,7 +647,7 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
self.update(idx_1, idx_2, step, cache);
return self.gmax - self.gmin > tol;
self.gmax - self.gmin > tol
}
None => false,
}
src/svm/svr.rs
+22 -30
View File
@@ -160,9 +160,9 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SVR<T, M, K> {
let (n, _) = x.shape();
if n != y.len() {
return Err(Failed::fit(&format!(
"Number of rows of X doesn't match number of rows of Y"
)));
return Err(Failed::fit(
&"Number of rows of X doesn\'t match number of rows of Y".to_string(),
));
}
let optimizer = Optimizer::new(x, y, &kernel, &parameters);
@@ -170,10 +170,10 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SVR<T, M, K> {
let (support_vectors, weight, b) = optimizer.smo();
Ok(SVR {
kernel: kernel,
kernel,
instances: support_vectors,
w: weight,
b: b,
b,
})
}
@@ -198,7 +198,7 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> SVR<T, M, K> {
f += self.w[i] * self.kernel.apply(&x, &self.instances[i]);
}
return f;
f
}
}
@@ -208,7 +208,7 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> PartialEq for SVR<
|| self.w.len() != other.w.len()
|| self.instances.len() != other.instances.len()
{
return false;
false
} else {
for i in 0..self.w.len() {
if (self.w[i] - other.w[i]).abs() > T::epsilon() {
@@ -220,7 +220,7 @@ impl<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> PartialEq for SVR<
return false;
}
}
return true;
true
}
}
}
@@ -230,7 +230,7 @@ impl<T: RealNumber, V: BaseVector<T>> SupportVector<T, V> {
let k_v = k.apply(&x, &x);
SupportVector {
index: i,
x: x,
x,
grad: [eps + y, eps - y],
k: k_v,
alpha: [T::zero(), T::zero()],
@@ -270,7 +270,7 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
gmaxindex: 0,
tau: T::from_f64(1e-12).unwrap(),
sv: support_vectors,
kernel: kernel,
kernel,
}
}
@@ -392,11 +392,9 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
self.sv[v2].alpha[j] = T::zero();
self.sv[v1].alpha[i] = diff;
}
} else {
if self.sv[v1].alpha[i] < T::zero() {
self.sv[v1].alpha[i] = T::zero();
self.sv[v2].alpha[j] = -diff;
}
} else if self.sv[v1].alpha[i] < T::zero() {
self.sv[v1].alpha[i] = T::zero();
self.sv[v2].alpha[j] = -diff;
}
if diff > T::zero() {
@@ -404,11 +402,9 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
self.sv[v1].alpha[i] = self.c;
self.sv[v2].alpha[j] = self.c - diff;
}
} else {
if self.sv[v2].alpha[j] > self.c {
self.sv[v2].alpha[j] = self.c;
self.sv[v1].alpha[i] = self.c + diff;
}
} else if self.sv[v2].alpha[j] > self.c {
self.sv[v2].alpha[j] = self.c;
self.sv[v1].alpha[i] = self.c + diff;
}
} else {
let delta = (self.sv[v1].grad[i] - self.sv[v2].grad[j]) / curv;
@@ -421,11 +417,9 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
self.sv[v1].alpha[i] = self.c;
self.sv[v2].alpha[j] = sum - self.c;
}
} else {
if self.sv[v2].alpha[j] < T::zero() {
self.sv[v2].alpha[j] = T::zero();
self.sv[v1].alpha[i] = sum;
}
} else if self.sv[v2].alpha[j] < T::zero() {
self.sv[v2].alpha[j] = T::zero();
self.sv[v1].alpha[i] = sum;
}
if sum > self.c {
@@ -433,11 +427,9 @@ impl<'a, T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> Optimizer<'a,
self.sv[v2].alpha[j] = self.c;
self.sv[v1].alpha[i] = sum - self.c;
}
} else {
if self.sv[v1].alpha[i] < T::zero() {
self.sv[v1].alpha[i] = T::zero();
self.sv[v2].alpha[j] = sum;
}
} else if self.sv[v1].alpha[i] < T::zero() {
self.sv[v1].alpha[i] = T::zero();
self.sv[v2].alpha[j] = sum;
}
}