From 2f67ef5c31caa84e2602de9941669130c03c6020 Mon Sep 17 00:00:00 2001
From: Federico Lolli <federico.lolli@skywarder.eu>
Date: Mon, 16 Oct 2023 16:55:19 +0200
Subject: [PATCH] [SFD] fixed eigen bugs (incorrect operators used)
---
src/shared/algorithms/SFD/SFDAscent.cpp | 10 ++++------
src/shared/algorithms/SFD/SFDAscent.h | 6 +++---
src/shared/algorithms/SFD/SFDDescent.cpp | 8 +++-----
src/shared/algorithms/SFD/SFDDescent.h | 6 +++---
src/shared/models/SVM.h | 2 +-
5 files changed, 14 insertions(+), 18 deletions(-)
diff --git a/src/shared/algorithms/SFD/SFDAscent.cpp b/src/shared/algorithms/SFD/SFDAscent.cpp
index fc5d6e54b..35b94568f 100644
--- a/src/shared/algorithms/SFD/SFDAscent.cpp
+++ b/src/shared/algorithms/SFD/SFDAscent.cpp
@@ -22,8 +22,6 @@
// ======= Sensor Fault Detection Model (SFDAscent) =======
-#pragma once
-
#include "SFDAscent.h"
#include <algorithms/FFT.h>
@@ -48,17 +46,17 @@ SFDAscent::FeaturesVec SFDAscent::getFeatures(const VectorIn& input)
u = data.mean();
x0 = data - u * VectorIn::Ones();
var = x0.squaredNorm() / lenChunk;
- s2 = x0.pow(2).mean();
- m4 = x0.pow(4).mean();
+ s2 = x0.array().pow(2).mean();
+ m4 = x0.array().pow(4).mean();
- rfourier = FFT32::fft(data); // TODO: fix complex -> float
+ rfourier = FFT32::fft(data).real(); // TODO: fix complex -> float
rfmean = rfourier.mean();
rfvar = (rfourier - rfmean * VectorIn::Ones()).squaredNorm() / lenChunk;
features(0) = delta;
features(1) = var;
features(2) = m4 / std::pow(s2, 2);
- features(3) = data.pow(5).mean();
+ features(3) = data.array().pow(5).mean();
features(4) = rfvar;
features(5) = rfourier.cwiseAbs().sum();
diff --git a/src/shared/algorithms/SFD/SFDAscent.h b/src/shared/algorithms/SFD/SFDAscent.h
index 970cf31c7..9640d0c8f 100644
--- a/src/shared/algorithms/SFD/SFDAscent.h
+++ b/src/shared/algorithms/SFD/SFDAscent.h
@@ -37,13 +37,13 @@ public:
static constexpr int numFeatures = 6;
static constexpr int lenChunk = 32;
- using SVM = SVM<numFeatures>;
+ using SVMn = SVM<numFeatures>;
using FeaturesVec = Eigen::Vector<float, numFeatures>;
using VectorIn = Eigen::Vector<float, lenChunk>;
struct SFDAConfig
{
- SVM::SVMConfig modelParameters;
+ SVMn::SVMConfig modelParameters;
};
SFDAscent(const SFDAConfig& config);
@@ -51,7 +51,7 @@ public:
bool classify(const VectorIn& input);
private:
- SVM svm;
+ SVMn svm;
FeaturesVec getFeatures(const VectorIn& input);
};
diff --git a/src/shared/algorithms/SFD/SFDDescent.cpp b/src/shared/algorithms/SFD/SFDDescent.cpp
index f88bc1682..7ea5283ac 100644
--- a/src/shared/algorithms/SFD/SFDDescent.cpp
+++ b/src/shared/algorithms/SFD/SFDDescent.cpp
@@ -22,8 +22,6 @@
// ======= Sensor Fault Detection Model (SFDAscent) =======
-#pragma once
-
#include "SFDDescent.h"
namespace Boardcore
@@ -47,9 +45,9 @@ SFDDescent::FeaturesVec SFDDescent::getFeatures(const VectorIn& input)
data(i) = (input(i) - min) / (std::max(delta, 1e-25f) * 2) - 1;
u = data.mean();
x0 = data - u * VectorIn::Ones();
- s2 = x0.pow(2).mean();
- m3 = x0.pow(3).mean();
- m4 = x0.pow(4).mean();
+ s2 = x0.array().pow(2).mean();
+ m3 = x0.array().pow(3).mean();
+ m4 = x0.array().pow(4).mean();
rms = std::sqrt(s2);
features(0) = data.cwiseAbs().maxCoeff() / rms;
diff --git a/src/shared/algorithms/SFD/SFDDescent.h b/src/shared/algorithms/SFD/SFDDescent.h
index 45c043a38..a0c332f4b 100644
--- a/src/shared/algorithms/SFD/SFDDescent.h
+++ b/src/shared/algorithms/SFD/SFDDescent.h
@@ -37,13 +37,13 @@ public:
static constexpr int numFeatures = 5;
static constexpr int lenChunk = 32;
- using SVM = SVM<numFeatures>;
+ using SVMn = SVM<numFeatures>;
using FeaturesVec = Eigen::Vector<float, numFeatures>;
using VectorIn = Eigen::Vector<float, lenChunk>;
struct SFDDConfig
{
- SVM::SVMConfig modelParameters;
+ SVMn::SVMConfig modelParameters;
};
SFDDescent(const SFDDConfig& config);
@@ -51,7 +51,7 @@ public:
bool classify(const VectorIn& input);
private:
- SVM svm;
+ SVMn svm;
FeaturesVec getFeatures(const VectorIn& input);
};
diff --git a/src/shared/models/SVM.h b/src/shared/models/SVM.h
index fe019d659..27594b3d6 100644
--- a/src/shared/models/SVM.h
+++ b/src/shared/models/SVM.h
@@ -51,7 +51,7 @@ public:
float score(VectorD input)
{
VectorD x = input - mu;
- x /= sigma;
+ x = x.array() / sigma.array();
return -((x / scale).dot(beta) + bias);
}
--
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