Package de.lmu.ifi.dbs.elki.math.linearalgebra

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.Vector.euclideanLength()

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    int idx = 0;
    // with 0 arguments
    {
      AffineTransformation aff = AffineTransformation.reorderAxesTransformation(v.getDimensionality(), new int[] {});
      Vector n = aff.apply(v).minus(ps[idx]);
      assertEquals("Permutation " + idx + " doesn't match.", n.euclideanLength(), 0.0, 0.001);
      idx++;
    }
    // with one argument
    for(int d1 = 1; d1 <= 3; d1++) {
      AffineTransformation aff = AffineTransformation.reorderAxesTransformation(v.getDimensionality(), new int[] { d1 });
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    }
    // with one argument
    for(int d1 = 1; d1 <= 3; d1++) {
      AffineTransformation aff = AffineTransformation.reorderAxesTransformation(v.getDimensionality(), new int[] { d1 });
      Vector n = aff.apply(v).minus(ps[idx]);
      assertEquals("Permutation " + idx + " doesn't match.", n.euclideanLength(), 0.0, 0.001);
      idx++;
    }
    // with two arguments
    for(int d1 = 1; d1 <= 3; d1++) {
      for(int d2 = 1; d2 <= 3; d2++) {
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        if(d1 == d2) {
          continue;
        }
        AffineTransformation aff = AffineTransformation.reorderAxesTransformation(v.getDimensionality(), new int[] { d1, d2 });
        Vector n = aff.apply(v).minus(ps[idx]);
        assertEquals("Permutation " + idx + " doesn't match.", n.euclideanLength(), 0.0, 0.001);
        idx++;
      }
    }
  }
}
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   * @param eigenPair the eigenpair to be normalized
   *
   */
  private void normalizeEigenPair(final EigenPair eigenPair) {
    final Vector eigenvector = eigenPair.getEigenvector();
    final double scaling = 1.0 / Math.sqrt(eigenPair.getEigenvalue()) * eigenvector.euclideanLength();
    eigenvector.timesEquals(scaling);
  }
}
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    int idx = 0;
    // with 0 arguments
    {
      AffineTransformation aff = AffineTransformation.reorderAxesTransformation(v.getDimensionality(), new int[] {});
      Vector n = aff.apply(v).minus(ps[idx]);
      assertEquals("Permutation " + idx + " doesn't match.", n.euclideanLength(), 0.0, 0.001);
      idx++;
    }
    // with one argument
    for(int d1 = 1; d1 <= 3; d1++) {
      AffineTransformation aff = AffineTransformation.reorderAxesTransformation(v.getDimensionality(), new int[] { d1 });
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    }
    // with one argument
    for(int d1 = 1; d1 <= 3; d1++) {
      AffineTransformation aff = AffineTransformation.reorderAxesTransformation(v.getDimensionality(), new int[] { d1 });
      Vector n = aff.apply(v).minus(ps[idx]);
      assertEquals("Permutation " + idx + " doesn't match.", n.euclideanLength(), 0.0, 0.001);
      idx++;
    }
    // with two arguments
    for(int d1 = 1; d1 <= 3; d1++) {
      for(int d2 = 1; d2 <= 3; d2++) {
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        if(d1 == d2) {
          continue;
        }
        AffineTransformation aff = AffineTransformation.reorderAxesTransformation(v.getDimensionality(), new int[] { d1, d2 });
        Vector n = aff.apply(v).minus(ps[idx]);
        assertEquals("Permutation " + idx + " doesn't match.", n.euclideanLength(), 0.0, 0.001);
        idx++;
      }
    }
  }
}
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   * @return the orthonormal basis generated by this method.
   * @throws RuntimeException if the given vectors are not linear independent.
   */
  private Matrix generateOrthonormalBasis(List<Vector> vectors) {
    Vector first = vectors.get(0);
    first = first.times(1.0 / first.euclideanLength());
    Matrix ret = new Matrix(first.getDimensionality(), vectors.size());
    ret.setCol(0, first);
    for(int i = 1; i < vectors.size(); i++) {
      // System.out.println("Matrix:" + ret);
      Vector v_i = vectors.get(i);
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