Package mikera.vectorz

Examples of mikera.vectorz.AVector

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import mikera.vectorz.Vectorz;

public class TestTransformz {

  @Test public void testConstantTransform() {
    AVector v=Vector.of(1,2,3,4);
    ATransform t=Transformz.constantTransform(3, v);
   
    AVector x=Vector3.of(10,11,12);
    AVector y=t.transform(x);
   
    assertEquals(y,v);
   
    // shouldn't have reference to original source vector
    v.fill(Double.NaN);
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      //OK
    }
  }
 
  private static void doApplyTest(ITransform t) {
    AVector x=Vectorz.createUniformRandomVector(t.inputDimensions());
    AVector y=Vectorz.newVector(t.outputDimensions());
    y.fill(Double.NaN);
   
    AVector x2=x.clone();
    t.transform(x2, y);
   
    // should overwrite everything
    for (int i=0; i<y.length(); i++) {
      assertNotEquals(Double.NaN,y.get(i));
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    t.hashCode();
  }
 
  private static void doSubTest(ATransform t) {
    ATransform st=t.takeComponents(Indexz.createRandomSubset(t.outputDimensions(), 0.5)); 
    AVector v=Vectorz.createUniformRandomVector(st.inputDimensions());
    st.transform(v);
  }
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    AVector v=Vectorz.createUniformRandomVector(st.inputDimensions());
    st.transform(v);
  }
 
  private static void doComponentTests(ATransform t) {
    AVector v=Vectorz.createUniformRandomVector(t.inputDimensions());
    AVector r=t.transform(v);
   
    for (int i=0; i<t.outputDimensions(); i++) {
      assertEquals(r.get(i),t.calculateElement(i, v),0.0);
    }
  }
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  }
 
  private static void doSizeTest(ATransform t) {
    assertEquals(t.inputDimensions()==t.outputDimensions(),t.isSquare());
   
    AVector iv=Vectorz.createUniformRandomVector(t.inputDimensions());
    AVector ov=Vectorz.createUniformRandomVector(t.outputDimensions());
   
    assertEquals(t.outputDimensions(),t.transform(iv).length());
   
    t.transform(iv, ov);
  }
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    t.transform(iv, ov);
  }
 
  private static void doComponentTest(ATransform t) {
    AVector iv=Vectorz.createUniformRandomVector(t.inputDimensions());
    AVector ov=Vectorz.createUniformRandomVector(t.outputDimensions());
   
    t.transform(iv, ov);
    for (int i=0; i<t.outputDimensions(); i++) {
      assertEquals(ov.get(i),t.calculateElement(i, iv),0.0);
    }
  }
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  protected AJoinedVector reconstruct(AVector... segments) {
    int sc=segmentCount();
    double[][] newData=new double[sc][];
    int[] offs=this.offsets.clone();
    for (int i=0; i<sc; i++) {
      AVector v=segments[i];
      if (v instanceof ADenseArrayVector) {
        newData[i]=((ADenseArrayVector) v).getArray();
        offs[i]=((ADenseArrayVector) v).getArrayOffset();
      } else {
        newData[i]=segments[i].toDoubleArray();
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   * @return The matrix's rank
   */
  public static int compute(AMatrix A, double threshold) {
    ISVDResult ans = SVD.decompose(A, true);
    int rank = 0;
    AVector singularValues = ans.getSingularValues();
    int n=singularValues.length();
    for(int i=0; i<n; i++) {
      if( singularValues.unsafeGet(i) >= threshold)
        rank++;
    }
    return rank;
  }
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     * @param threshold Tolerance used to determine if a singular value is singular.
     * @return The matrix's rank
     */
  public static int compute(ISVDResult result, double threshold) {
    int rank = 0;
    AVector singularValues = result.getSingularValues();
        for( double s : singularValues ) {
            if( s > threshold)
                rank++;
        }
        return rank;
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    if (start>=split) return right.subVector(start-split, length);
   
    if ((start+length)<=split) return left.subVector(start, length);
   
    int cut=split-start; // amount cut from left vector
    AVector v1=left.subVector(start, cut);
    AVector v2=right.subVector(0, length-cut);
    return v1.join(v2);
  }
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Related Classes of mikera.vectorz.AVector

  • example.SparseMatrix
  • mikera.arrayz.impl.AbstractArray
  • mikera.arrayz.impl.SliceArray
  • mikera.arrayz.INDArray
  • mikera.arrayz.NDArray
  • mikera.arrayz.TestArrays
  • mikera.arrayz.TestMiscArrayOps
  • mikera.indexz.Index
  • mikera.matrixx.algo.Rank
  • mikera.matrixx.algo.TestLinear
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