Replaced cwiseProduct() with array().square(). Killed deprecated comments.

gtsam/base/Matrix.cpp

@@ -339,7 +339,7 @@ weighted_eliminate(Matrix& A, Vector& b, const Vector& sigmas) {
   list<boost::tuple<Vector, double, double> > results;
 
   Vector pseudo(m); // allocate storage for pseudo-inverse
-  Vector weights = reciprocal(sigmas.cwiseProduct(sigmas)); // calculate weights once
+  Vector weights = reciprocal(sigmas.array().square()); // calculate weights once
 
   // We loop over all columns, because the columns that can be eliminated
   // are not necessarily contiguous. For each one, estimate the corresponding

gtsam/base/Vector.h

@@ -213,12 +213,6 @@ GTSAM_EXPORT ConstSubVector sub(const Vector &v, size_t i1, size_t i2);
 GTSAM_EXPORT void subInsert(Vector& fullVector, const Vector& subVector, size_t i);
 
 #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V4
-/**
- * elementwise multiplication
- * @param a first vector
- * @param b second vector
- * @return vector [a(i)*b(i)]
- */
 GTSAM_EXPORT Vector emul(const Vector &a, const Vector &b);
 #endif
 

gtsam/base/tests/testVector.cpp

@@ -198,7 +198,7 @@ TEST(Vector, weightedPseudoinverse )
   // create sigmas
   Vector sigmas(2);
   sigmas(0) = 0.1; sigmas(1) = 0.2;
-  Vector weights = reciprocal(sigmas.cwiseProduct(sigmas));
+  Vector weights = reciprocal(sigmas.array().square());
 
   // perform solve
   Vector actual; double precision;
@@ -224,7 +224,7 @@ TEST(Vector, weightedPseudoinverse_constraint )
   // create sigmas
   Vector sigmas(2);
   sigmas(0) = 0.0; sigmas(1) = 0.2;
-  Vector weights = reciprocal(sigmas.cwiseProduct(sigmas));
+  Vector weights = reciprocal(sigmas.array().square());
   // perform solve
   Vector actual; double precision;
   boost::tie(actual, precision) = weightedPseudoinverse(x, weights);
@@ -243,7 +243,7 @@ TEST(Vector, weightedPseudoinverse_nan )
 {
   Vector a = (Vector(4) << 1., 0., 0., 0.).finished();
   Vector sigmas = (Vector(4) << 0.1, 0.1, 0., 0.).finished();
-  Vector weights = reciprocal(sigmas.cwiseProduct(sigmas));
+  Vector weights = reciprocal(sigmas.array().square());
   Vector pseudo; double precision;
   boost::tie(pseudo, precision) = weightedPseudoinverse(a, weights);
 

gtsam/linear/tests/testKalmanFilter.cpp

@@ -270,7 +270,7 @@ TEST( KalmanFilter, QRvsCholesky ) {
   EXPECT(assert_equal(expected2, pb2->covariance(), 1e-7));
 
   // do the above update again, this time with a full Matrix Q
-  Matrix modelQ = diag(sigmas.cwiseProduct(sigmas));
+  Matrix modelQ = diag(sigmas.array().square());
   KalmanFilter::State pa3 = kfa.updateQ(pa, H, z, modelQ);
   KalmanFilter::State pb3 = kfb.updateQ(pb, H, z, modelQ);
 

gtsam_unstable/slam/AHRS.cpp

@@ -186,8 +186,7 @@ std::pair<Mechanization_bRn2, KalmanFilter::State> AHRS::aid(
     Matrix b_g = bRn * n_g_cross_;
     H = collect(3, &b_g, &Z_3x3, &I_3x3);
     // And the measurement noise, TODO: should be created once where sigmas_v_a is given
-    R = diag(sigmas_v_a_.cwiseQuotient(sigmas_v_a_));
-
+    R = diag(sigmas_v_a_.array().square());
   }
 
 // update the Kalman filter