private void setTransform(
TransformType t, double[] normMul, double[] normSub, int vecStart, int n) {
int idx = 0; // idx!=i when interactions are in play, otherwise, it's just 'i'
for (int i = 0; i < n; ++i) {
Vec v = _adaptedFrame.vec(vecStart + i);
boolean isIWV = isInteractionVec(vecStart + i);
switch (t) {
case STANDARDIZE:
normMul[idx] = (v.sigma() != 0) ? 1.0 / v.sigma() : 1.0;
if (isIWV) for (int j = idx + 1; j < nextNumericIdx(i) + idx; j++) normMul[j] = 1;
normSub[idx] = v.mean();
break;
case NORMALIZE:
normMul[idx] = (v.max() - v.min() > 0) ? 1.0 / (v.max() - v.min()) : 1.0;
if (isIWV) for (int j = idx + 1; j < nextNumericIdx(i) + idx; j++) normMul[j] = 1;
normSub[idx] = v.mean();
break;
case DEMEAN:
normMul[idx] = 1;
if (isIWV) for (int j = idx + 1; j < nextNumericIdx(i) + idx; j++) normMul[j] = 1;
normSub[idx] = v.mean();
break;
case DESCALE:
normMul[idx] = (v.sigma() != 0) ? 1.0 / v.sigma() : 1.0;
if (isIWV) for (int j = idx + 1; j < nextNumericIdx(i) + idx; j++) normMul[j] = 1;
normSub[idx] = 0;
break;
default:
throw H2O.unimpl();
}
assert !Double.isNaN(normMul[idx]);
assert !Double.isNaN(normSub[idx]);
idx = isIWV ? (idx + nextNumericIdx(i)) : (idx + 1);
}
}
@Test
public void test() {
Frame frame = null;
try {
Futures fs = new Futures();
Random random = new Random();
Vec[] vecs = new Vec[1];
AppendableVec vec = new AppendableVec(Vec.newKey(), Vec.T_NUM);
for (int i = 0; i < 2; i++) {
NewChunk chunk = new NewChunk(vec, i);
for (int r = 0; r < 1000; r++) chunk.addNum(random.nextInt(1000));
chunk.close(i, fs);
}
vecs[0] = vec.layout_and_close(fs);
fs.blockForPending();
frame = new Frame(Key.<Frame>make(), null, vecs);
// Make sure we test the multi-chunk case
vecs = frame.vecs();
assert vecs[0].nChunks() > 1;
long rows = frame.numRows();
Vec v = vecs[0];
double min = Double.POSITIVE_INFINITY, max = Double.NEGATIVE_INFINITY, mean = 0, sigma = 0;
for (int r = 0; r < rows; r++) {
double d = v.at(r);
if (d < min) min = d;
if (d > max) max = d;
mean += d;
}
mean /= rows;
for (int r = 0; r < rows; r++) {
double d = v.at(r);
sigma += (d - mean) * (d - mean);
}
sigma = Math.sqrt(sigma / (rows - 1));
double epsilon = 1e-9;
assertEquals(max, v.max(), epsilon);
assertEquals(min, v.min(), epsilon);
assertEquals(mean, v.mean(), epsilon);
assertEquals(sigma, v.sigma(), epsilon);
} finally {
if (frame != null) frame.delete();
}
}