/// <summary>
/// Test linear extrapolation without clamped points
/// </summary>
//JAVA TO C# CONVERTER TODO TASK: Most Java annotations will not have direct .NET equivalent attributes:
//ORIGINAL LINE: @Test public void linearExtrapolationNoClampedTest()
public virtual void linearExtrapolationNoClampedTest()
{
double[] xValues = new double[] { -5.0, -1.4, 3.2, 3.5, 7.6 };
double[] yValues = new double[] { -2.2, 1.1, 1.9, 2.3, -0.1 };
int nData = xValues.Length;
int nKeys = 20;
double interval = (3.0 * xValues[nData - 1] - xValues[nData - 1]) / (nKeys - 1);
double[] keys = new double[nKeys];
int n = INTERP.Length;
for (int i = 0; i < n; ++i)
{
ProductPiecewisePolynomialInterpolator interp = new ProductPiecewisePolynomialInterpolator(INTERP[i]);
PiecewisePolynomialResult result = interp.interpolate(xValues, yValues);
for (int j = 0; j < nKeys; ++j)
{
keys[j] = xValues[nData - 1] + j * interval;
}
double[] values = FUNC.evaluate(result, keys).row(0).toArray();
for (int j = 2; j < nKeys; ++j)
{
InterpolatorTestUtil.assertRelative("linearExtrapolationTest", values[j - 1] - values[j - 2], values[j - 1] - values[j - 2], EPS);
}
}
n = INTERP_SENSE.Length;
for (int i = 0; i < n; ++i)
{
ProductPiecewisePolynomialInterpolator interp = new ProductPiecewisePolynomialInterpolator(INTERP_SENSE[i]);
PiecewisePolynomialResultsWithSensitivity result = interp.interpolateWithSensitivity(xValues, yValues);
for (int j = 0; j < nKeys; ++j)
{
keys[j] = xValues[nData - 1] + j * interval;
}
double[] values = FUNC.evaluate(result, keys).row(0).toArray();
for (int j = 2; j < nKeys; ++j)
{
InterpolatorTestUtil.assertRelative("linearExtrapolationTest", values[j - 1] - values[j - 2], values[j - 1] - values[j - 2], EPS);
}
DoubleArray[] sense = FUNC.nodeSensitivity(result, keys);
for (int k = 0; k < nData; ++k)
{
double[] yValuesUp = Arrays.copyOf(yValues, nData);
double[] yValuesDw = Arrays.copyOf(yValues, nData);
yValuesUp[k] += DELTA / xValues[k];
yValuesDw[k] -= DELTA / xValues[k];
PiecewisePolynomialResultsWithSensitivity resultUp = interp.interpolateWithSensitivity(xValues, yValuesUp);
PiecewisePolynomialResultsWithSensitivity resultDw = interp.interpolateWithSensitivity(xValues, yValuesDw);
double[] tmpUp = FUNC.evaluate(resultUp, keys).rowArray(0);
double[] tmpDw = FUNC.evaluate(resultDw, keys).rowArray(0);
for (int l = 0; l < nKeys; ++l)
{
double res = 0.5 * (tmpUp[l] - tmpDw[l]) / DELTA; // lk
InterpolatorTestUtil.assertRelative("linearExtrapolationTest", sense[l].get(k), res, DELTA);
}
}
}
}
/// <summary>
/// No clamped points added
/// </summary>
//JAVA TO C# CONVERTER TODO TASK: Most Java annotations will not have direct .NET equivalent attributes:
//ORIGINAL LINE: @Test public void notClampedTest()
public virtual void notClampedTest()
{
double[][] xValuesSet = new double[][]
{
new double[] { -5.0, -1.4, 3.2, 3.5, 7.6 },
new double[] { 1.0, 2.0, 4.5, 12.1, 14.2 },
new double[] { -5.2, -3.4, -3.2, -0.9, -0.2 }
};
double[][] yValuesSet = new double[][]
{
new double[] { -2.2, 1.1, 1.9, 2.3, -0.1 },
new double[] { 3.4, 5.2, 4.3, 1.1, 0.2 },
new double[] { 1.4, 2.2, 4.1, 1.9, 0.99 }
};
for (int k = 0; k < xValuesSet.Length; ++k)
{
double[] xValues = Arrays.copyOf(xValuesSet[k], xValuesSet[k].Length);
double[] yValues = Arrays.copyOf(yValuesSet[k], yValuesSet[k].Length);
int nData = xValues.Length;
double[] xyValues = new double[nData];
for (int j = 0; j < nData; ++j)
{
xyValues[j] = xValues[j] * yValues[j];
}
int nKeys = 100;
double interval = (xValues[nData - 1] - xValues[0]) / (nKeys - 1.0);
int n = INTERP.Length;
for (int i = 0; i < n; ++i)
{
ProductPiecewisePolynomialInterpolator interp = new ProductPiecewisePolynomialInterpolator(INTERP[i]);
for (int j = 0; j < nKeys; ++j)
{
double key = xValues[0] + interval * j;
InterpolatorTestUtil.assertRelative("notClampedTest", INTERP[i].interpolate(xValues, xyValues, key), interp.interpolate(xValues, yValues, key), EPS);
}
}
n = INTERP_SENSE.Length;
for (int i = 0; i < n; ++i)
{
ProductPiecewisePolynomialInterpolator interp = new ProductPiecewisePolynomialInterpolator(INTERP_SENSE[i]);
PiecewisePolynomialResultsWithSensitivity result = interp.interpolateWithSensitivity(xValues, yValues);
PiecewisePolynomialResultsWithSensitivity resultBase = INTERP_SENSE[i].interpolateWithSensitivity(xValues, xyValues);
for (int j = 0; j < nKeys; ++j)
{
double key = xValues[0] + interval * j;
InterpolatorTestUtil.assertRelative("notClampedTest", FUNC.evaluate(resultBase, key).get(0), FUNC.evaluate(result, key).get(0), EPS);
InterpolatorTestUtil.assertArrayRelative("notClampedTest", FUNC.nodeSensitivity(resultBase, key).toArray(), FUNC.nodeSensitivity(result, key).toArray(), EPS);
}
}
}
}
/// <summary>
/// Clamped points
/// </summary>
//JAVA TO C# CONVERTER TODO TASK: Most Java annotations will not have direct .NET equivalent attributes:
//ORIGINAL LINE: @Test public void clampedTest()
public virtual void clampedTest()
{
double[] xValues = new double[] { -5.0, -1.4, 3.2, 3.5, 7.6 };
double[] yValues = new double[] { -2.2, 1.1, 1.9, 2.3, -0.1 };
double[][] xValuesClampedSet = new double[][]
{
new double[] { 0.0 },
new double[] { -7.2, -2.5, 8.45 },
new double[] {}
};
double[][] yValuesClampedSet = new double[][]
{
new double[] { 0.0 },
new double[] { -1.2, -1.4, 2.2 },
new double[] {}
};
for (int k = 0; k < xValuesClampedSet.Length; ++k)
{
double[] xValuesClamped = Arrays.copyOf(xValuesClampedSet[k], xValuesClampedSet[k].Length);
double[] yValuesClamped = Arrays.copyOf(yValuesClampedSet[k], yValuesClampedSet[k].Length);
int nData = xValues.Length;
int nClamped = xValuesClamped.Length;
int nTotal = nData + nClamped;
double[] xValuesForBase = new double[nTotal];
double[] yValuesForBase = new double[nTotal];
Array.Copy(xValues, 0, xValuesForBase, 0, nData);
Array.Copy(yValues, 0, yValuesForBase, 0, nData);
Array.Copy(xValuesClamped, 0, xValuesForBase, nData, nClamped);
Array.Copy(yValuesClamped, 0, yValuesForBase, nData, nClamped);
DoubleArrayMath.sortPairs(xValuesForBase, yValuesForBase);
double[] xyValuesBase = new double[nTotal];
for (int j = 0; j < nTotal; ++j)
{
xyValuesBase[j] = xValuesForBase[j] * yValuesForBase[j];
}
int nKeys = 100;
double interval = (xValues[nData - 1] - xValues[0]) / (nKeys - 1.0);
int n = INTERP.Length;
for (int i = 0; i < n; ++i)
{
ProductPiecewisePolynomialInterpolator interp = new ProductPiecewisePolynomialInterpolator(INTERP[i], xValuesClamped, yValuesClamped);
for (int j = 0; j < nKeys; ++j)
{
double key = xValues[0] + interval * j;
InterpolatorTestUtil.assertRelative("clampedTest", INTERP[i].interpolate(xValuesForBase, xyValuesBase, key), interp.interpolate(xValues, yValues, key), EPS);
}
}
n = INTERP_SENSE.Length;
for (int i = 0; i < n; ++i)
{
ProductPiecewisePolynomialInterpolator interp = new ProductPiecewisePolynomialInterpolator(INTERP_SENSE[i], xValuesClamped, yValuesClamped);
PiecewisePolynomialResultsWithSensitivity result = interp.interpolateWithSensitivity(xValues, yValues);
PiecewisePolynomialResultsWithSensitivity resultBase = INTERP_SENSE[i].interpolateWithSensitivity(xValuesForBase, xyValuesBase);
for (int j = 0; j < nKeys; ++j)
{
double key = xValues[0] + interval * j;
InterpolatorTestUtil.assertRelative("clampedTest", FUNC.evaluate(resultBase, key).get(0), FUNC.evaluate(result, key).get(0), EPS);
InterpolatorTestUtil.assertArrayRelative("clampedTest", FUNC.nodeSensitivity(resultBase, key).toArray(), FUNC.nodeSensitivity(result, key).toArray(), EPS);
}
}
}
}