From eddf8eb5e4facbcb731edd8f83e10961f586aef4 Mon Sep 17 00:00:00 2001
From: Manuel Gunther <siebenkopf@googlemail.com>
Date: Tue, 6 Jun 2017 14:15:44 -0600
Subject: [PATCH] Now use checked functions from bob::math
---
bob/learn/linear/cpp/lda.cpp | 10 +++++-----
bob/learn/linear/cpp/machine.cpp | 2 +-
bob/learn/linear/cpp/pca.cpp | 6 +++---
3 files changed, 9 insertions(+), 9 deletions(-)
diff --git a/bob/learn/linear/cpp/lda.cpp b/bob/learn/linear/cpp/lda.cpp
index dee634e..1cbf955 100644
--- a/bob/learn/linear/cpp/lda.cpp
+++ b/bob/learn/linear/cpp/lda.cpp
@@ -127,7 +127,7 @@ namespace bob { namespace learn { namespace linear {
blitz::Array<double,1> preMean(n_features);
blitz::Array<double,2> Sw(n_features, n_features);
blitz::Array<double,2> Sb(n_features, n_features);
- bob::math::scatters_(data, Sw, Sb, preMean);
+ bob::math::scatters(data, Sw, Sb, preMean);
// computes the generalized eigenvalue decomposition
// so to find the eigen vectors/values of Sw^(-1) * Sb
@@ -137,11 +137,11 @@ namespace bob { namespace learn { namespace linear {
if (m_use_pinv) {
//note: misuse V and Sw as temporary place holders for data
- bob::math::pinv_(Sw, V); //V now contains Sw^-1
- bob::math::prod_(V, Sb, Sw); //Sw now contains Sw^-1*Sb
+ bob::math::pinv(Sw, V); //V now contains Sw^-1
+ bob::math::prod(V, Sb, Sw); //Sw now contains Sw^-1*Sb
blitz::Array<std::complex<double>,1> Dtemp(eigen_values_.shape());
blitz::Array<std::complex<double>,2> Vtemp(V.shape());
- bob::math::eig_(Sw, Vtemp, Dtemp); //V now contains eigen-vectors
+ bob::math::eig(Sw, Vtemp, Dtemp); //V now contains eigen-vectors
//sorting: we know this problem on has real eigen-values
blitz::Range a = blitz::Range::all();
@@ -153,7 +153,7 @@ namespace bob { namespace learn { namespace linear {
}
}
else {
- bob::math::eigSym_(Sb, Sw, V, eigen_values_);
+ bob::math::eigSym(Sb, Sw, V, eigen_values_);
}
// Convert ascending order to descending order
diff --git a/bob/learn/linear/cpp/machine.cpp b/bob/learn/linear/cpp/machine.cpp
index 213a5b7..91e4994 100644
--- a/bob/learn/linear/cpp/machine.cpp
+++ b/bob/learn/linear/cpp/machine.cpp
@@ -155,7 +155,7 @@ namespace bob { namespace learn { namespace linear {
void Machine::forward_ (const blitz::Array<double,1>& input, blitz::Array<double,1>& output) const {
m_buffer = (input - m_input_sub) / m_input_div;
- bob::math::prod_(m_buffer, m_weight, output);
+ bob::math::prod(m_buffer, m_weight, output);
for (int i=0; i<m_weight.extent(1); ++i)
output(i) = m_activation->f(output(i) + m_bias(i));
diff --git a/bob/learn/linear/cpp/pca.cpp b/bob/learn/linear/cpp/pca.cpp
index fa21bc4..33147fa 100644
--- a/bob/learn/linear/cpp/pca.cpp
+++ b/bob/learn/linear/cpp/pca.cpp
@@ -68,12 +68,12 @@ namespace bob { namespace learn { namespace linear {
*/
blitz::Array<double,1> mean(X.extent(1));
blitz::Array<double,2> Sigma(X.extent(1), X.extent(1));
- bob::math::scatter_(X, Sigma, mean);
+ bob::math::scatter(X, Sigma, mean);
Sigma /= (X.extent(0)-1); //unbiased variance estimator
blitz::Array<double,2> U(X.extent(1), X.extent(1));
blitz::Array<double,1> e(X.extent(1));
- bob::math::eigSym_(Sigma, U, e);
+ bob::math::eigSym(Sigma, U, e);
e.reverseSelf(0);
U.reverseSelf(1);
@@ -123,7 +123,7 @@ namespace bob { namespace learn { namespace linear {
const int rank_1 = (rank == (int)X.extent(1))? X.extent(1) : X.extent(0);
blitz::Array<double,2> U(X.extent(1), rank_1);
blitz::Array<double,1> sigma(rank_1);
- bob::math::svd_(data, U, sigma, safe_svd);
+ bob::math::svd(data, U, sigma, safe_svd);
/**
* sets the linear machine with the results:
--
GitLab