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Example 41 with IntFloatVector

use of com.tencent.angel.ml.math2.vector.IntFloatVector in project angel by Tencent.

the class DotMatrixExecutor method apply.

private static Vector apply(BlasFloatMatrix mat, boolean trans, IntLongVector v) {
    int m = mat.getNumRows(), n = mat.getNumCols();
    float[] resArr;
    if (trans) {
        assert m == v.getDim();
        resArr = new float[n];
    } else {
        assert n == v.getDim();
        resArr = new float[m];
    }
    int r = mat.getNumRows(), c = mat.getNumCols();
    float[] data = mat.getData();
    if (v.isDense()) {
        float[] tempArray = ArrayCopy.copy(v.getStorage().getValues(), new float[v.getDim()]);
        if (trans) {
            blas.sgemv("N", c, r, 1.0f, data, c, tempArray, 1, 0.0f, resArr, 1);
        } else {
            blas.sgemv("T", c, r, 1.0f, data, c, tempArray, 1, 0.0f, resArr, 1);
        }
    } else if (v.isSparse()) {
        if (trans) {
            for (int j = 0; j < c; j++) {
                ObjectIterator<Int2LongMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2LongMap.Entry entry = iter.next();
                    int i = entry.getIntKey();
                    resArr[j] += data[i * c + j] * entry.getLongValue();
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                ObjectIterator<Int2LongMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2LongMap.Entry entry = iter.next();
                    int j = entry.getIntKey();
                    resArr[i] += data[i * c + j] * entry.getLongValue();
                }
            }
        }
    } else {
        // sorted
        if (trans) {
            for (int j = 0; j < r; j++) {
                int[] idxs = v.getStorage().getIndices();
                long[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[j] += data[idxs[k] * c + j] * vals[k];
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                int[] idxs = v.getStorage().getIndices();
                long[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[i] += data[i * c + idxs[k]] * vals[k];
                }
            }
        }
    }
    IntFloatDenseVectorStorage storage = new IntFloatDenseVectorStorage(resArr);
    return new IntFloatVector(v.getMatrixId(), v.getClock(), 0, resArr.length, storage);
}
Also used : IntFloatDenseVectorStorage(com.tencent.angel.ml.math2.storage.IntFloatDenseVectorStorage) Int2LongMap(it.unimi.dsi.fastutil.ints.Int2LongMap) IntFloatVector(com.tencent.angel.ml.math2.vector.IntFloatVector) ObjectIterator(it.unimi.dsi.fastutil.objects.ObjectIterator)

Example 42 with IntFloatVector

use of com.tencent.angel.ml.math2.vector.IntFloatVector in project angel by Tencent.

the class DotMatrixExecutor method apply.

private static Vector apply(BlasFloatMatrix mat, boolean trans, IntDummyVector v) {
    int m = mat.getNumRows(), n = mat.getNumCols();
    float[] resArr;
    if (trans) {
        assert m == v.getDim();
        resArr = new float[n];
    } else {
        assert n == v.getDim();
        resArr = new float[m];
    }
    int r = mat.getNumRows(), c = mat.getNumCols();
    float[] data = mat.getData();
    if (trans) {
        for (int j = 0; j < c; j++) {
            int[] idxs = v.getIndices();
            for (int i : idxs) {
                resArr[j] += data[i * c + j];
            }
        }
    } else {
        for (int i = 0; i < r; i++) {
            int[] idxs = v.getIndices();
            for (int j : idxs) {
                resArr[i] += data[i * c + j];
            }
        }
    }
    IntFloatDenseVectorStorage storage = new IntFloatDenseVectorStorage(resArr);
    return new IntFloatVector(v.getMatrixId(), v.getClock(), 0, resArr.length, storage);
}
Also used : IntFloatDenseVectorStorage(com.tencent.angel.ml.math2.storage.IntFloatDenseVectorStorage) IntFloatVector(com.tencent.angel.ml.math2.vector.IntFloatVector)

Example 43 with IntFloatVector

use of com.tencent.angel.ml.math2.vector.IntFloatVector in project angel by Tencent.

the class DotMatrixExecutor method apply.

private static Vector apply(BlasFloatMatrix mat, boolean trans, IntFloatVector v) {
    int m = mat.getNumRows(), n = mat.getNumCols();
    float[] resArr;
    if (trans) {
        assert m == v.getDim();
        resArr = new float[n];
    } else {
        assert n == v.getDim();
        resArr = new float[m];
    }
    int r = mat.getNumRows(), c = mat.getNumCols();
    float[] data = mat.getData();
    if (v.isDense()) {
        float[] tempArray = v.getStorage().getValues();
        if (trans) {
            blas.sgemv("N", c, r, 1.0f, data, c, tempArray, 1, 0.0f, resArr, 1);
        } else {
            blas.sgemv("T", c, r, 1.0f, data, c, tempArray, 1, 0.0f, resArr, 1);
        }
    } else if (v.isSparse()) {
        if (trans) {
            for (int j = 0; j < c; j++) {
                ObjectIterator<Int2FloatMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2FloatMap.Entry entry = iter.next();
                    int i = entry.getIntKey();
                    resArr[j] += data[i * c + j] * entry.getFloatValue();
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                ObjectIterator<Int2FloatMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2FloatMap.Entry entry = iter.next();
                    int j = entry.getIntKey();
                    resArr[i] += data[i * c + j] * entry.getFloatValue();
                }
            }
        }
    } else {
        // sorted
        if (trans) {
            for (int j = 0; j < r; j++) {
                int[] idxs = v.getStorage().getIndices();
                float[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[j] += data[idxs[k] * c + j] * vals[k];
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                int[] idxs = v.getStorage().getIndices();
                float[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[i] += data[i * c + idxs[k]] * vals[k];
                }
            }
        }
    }
    IntFloatDenseVectorStorage storage = new IntFloatDenseVectorStorage(resArr);
    return new IntFloatVector(v.getMatrixId(), v.getClock(), 0, resArr.length, storage);
}
Also used : IntFloatDenseVectorStorage(com.tencent.angel.ml.math2.storage.IntFloatDenseVectorStorage) Int2FloatMap(it.unimi.dsi.fastutil.ints.Int2FloatMap) IntFloatVector(com.tencent.angel.ml.math2.vector.IntFloatVector) ObjectIterator(it.unimi.dsi.fastutil.objects.ObjectIterator)

Example 44 with IntFloatVector

use of com.tencent.angel.ml.math2.vector.IntFloatVector in project angel by Tencent.

the class DotMatrixExecutor method apply.

private static Vector apply(BlasFloatMatrix mat, boolean trans, IntIntVector v) {
    int m = mat.getNumRows(), n = mat.getNumCols();
    float[] resArr;
    if (trans) {
        assert m == v.getDim();
        resArr = new float[n];
    } else {
        assert n == v.getDim();
        resArr = new float[m];
    }
    int r = mat.getNumRows(), c = mat.getNumCols();
    float[] data = mat.getData();
    if (v.isDense()) {
        float[] tempArray = ArrayCopy.copy(v.getStorage().getValues(), new float[v.getDim()]);
        if (trans) {
            blas.sgemv("N", c, r, 1.0f, data, c, tempArray, 1, 0.0f, resArr, 1);
        } else {
            blas.sgemv("T", c, r, 1.0f, data, c, tempArray, 1, 0.0f, resArr, 1);
        }
    } else if (v.isSparse()) {
        if (trans) {
            for (int j = 0; j < c; j++) {
                ObjectIterator<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2IntMap.Entry entry = iter.next();
                    int i = entry.getIntKey();
                    resArr[j] += data[i * c + j] * entry.getIntValue();
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                ObjectIterator<Int2IntMap.Entry> iter = v.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2IntMap.Entry entry = iter.next();
                    int j = entry.getIntKey();
                    resArr[i] += data[i * c + j] * entry.getIntValue();
                }
            }
        }
    } else {
        // sorted
        if (trans) {
            for (int j = 0; j < r; j++) {
                int[] idxs = v.getStorage().getIndices();
                int[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[j] += data[idxs[k] * c + j] * vals[k];
                }
            }
        } else {
            for (int i = 0; i < r; i++) {
                int[] idxs = v.getStorage().getIndices();
                int[] vals = v.getStorage().getValues();
                for (int k = 0; k < idxs.length; k++) {
                    resArr[i] += data[i * c + idxs[k]] * vals[k];
                }
            }
        }
    }
    IntFloatDenseVectorStorage storage = new IntFloatDenseVectorStorage(resArr);
    return new IntFloatVector(v.getMatrixId(), v.getClock(), 0, resArr.length, storage);
}
Also used : IntFloatDenseVectorStorage(com.tencent.angel.ml.math2.storage.IntFloatDenseVectorStorage) Int2IntMap(it.unimi.dsi.fastutil.ints.Int2IntMap) IntFloatVector(com.tencent.angel.ml.math2.vector.IntFloatVector) ObjectIterator(it.unimi.dsi.fastutil.objects.ObjectIterator)

Example 45 with IntFloatVector

use of com.tencent.angel.ml.math2.vector.IntFloatVector in project angel by Tencent.

the class SimpleBinaryOutAllExecutor method apply.

public static Vector apply(IntDoubleVector v1, IntFloatVector v2, Binary op) {
    IntDoubleVector res;
    if (v1.isDense() && v2.isDense()) {
        res = v1.copy();
        double[] resValues = res.getStorage().getValues();
        float[] v2Values = v2.getStorage().getValues();
        for (int idx = 0; idx < resValues.length; idx++) {
            resValues[idx] = op.apply(resValues[idx], v2Values[idx]);
        }
    } else if (v1.isDense() && v2.isSparse()) {
        res = v1.copy();
        double[] resValues = res.getStorage().getValues();
        if (v2.size() < Constant.denseLoopThreshold * v2.getDim()) {
            for (int i = 0; i < resValues.length; i++) {
                resValues[i] = op.apply(resValues[i], 0);
            }
            ObjectIterator<Int2FloatMap.Entry> iter = v2.getStorage().entryIterator();
            while (iter.hasNext()) {
                Int2FloatMap.Entry entry = iter.next();
                int idx = entry.getIntKey();
                resValues[idx] = op.apply(v1.get(idx), entry.getFloatValue());
            }
        } else {
            IntFloatVectorStorage v2Storage = v2.getStorage();
            for (int i = 0; i < resValues.length; i++) {
                if (v2Storage.hasKey(i)) {
                    resValues[i] = op.apply(resValues[i], v2.get(i));
                } else {
                    resValues[i] = op.apply(resValues[i], 0);
                }
            }
        }
    } else if (v1.isDense() && v2.isSorted()) {
        res = v1.copy();
        double[] resValues = res.getStorage().getValues();
        if (v2.size() < Constant.denseLoopThreshold * v2.getDim()) {
            int[] v2Indices = v2.getStorage().getIndices();
            float[] v2Values = v2.getStorage().getValues();
            for (int i = 0; i < resValues.length; i++) {
                resValues[i] = op.apply(resValues[i], 0);
            }
            int size = v2.size();
            for (int i = 0; i < size; i++) {
                int idx = v2Indices[i];
                resValues[idx] = op.apply(v1.get(idx), v2Values[i]);
            }
        } else {
            IntFloatVectorStorage v2Storage = v2.getStorage();
            for (int i = 0; i < resValues.length; i++) {
                if (v2Storage.hasKey(i)) {
                    resValues[i] = op.apply(resValues[i], v2.get(i));
                } else {
                    resValues[i] = op.apply(resValues[i], 0);
                }
            }
        }
    } else if (v1.isSparse() && v2.isDense()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            IntDoubleVectorStorage newStorage = v1.getStorage().emptyDense();
            double[] resValues = newStorage.getValues();
            float[] v2Values = v2.getStorage().getValues();
            if (v1.size() < Constant.denseLoopThreshold * v1.getDim()) {
                for (int i = 0; i < resValues.length; i++) {
                    resValues[i] = op.apply(0, v2Values[i]);
                }
                ObjectIterator<Int2DoubleMap.Entry> iter = v1.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2DoubleMap.Entry entry = iter.next();
                    int idx = entry.getIntKey();
                    resValues[idx] = op.apply(entry.getDoubleValue(), v2Values[idx]);
                }
            } else {
                for (int i = 0; i < resValues.length; i++) {
                    if (v1.getStorage().hasKey(i)) {
                        resValues[i] = op.apply(v1.get(i), v2Values[i]);
                    } else {
                        resValues[i] = op.apply(0, v2Values[i]);
                    }
                }
            }
            res = new IntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else if (v1.isSorted() && v2.isDense()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            IntDoubleVectorStorage newStorage = v1.getStorage().emptyDense();
            double[] resValues = newStorage.getValues();
            float[] v2Values = v2.getStorage().getValues();
            if (v1.size() < Constant.denseLoopThreshold * v1.getDim()) {
                int[] v1Indices = v1.getStorage().getIndices();
                double[] v1Values = v1.getStorage().getValues();
                for (int i = 0; i < resValues.length; i++) {
                    resValues[i] = op.apply(0, v2Values[i]);
                }
                int size = v1.size();
                for (int i = 0; i < size; i++) {
                    int idx = v1Indices[i];
                    resValues[idx] = op.apply(v1Values[i], v2Values[idx]);
                }
            } else {
                IntDoubleVectorStorage v1Storage = v1.getStorage();
                for (int i = 0; i < resValues.length; i++) {
                    if (v1Storage.hasKey(i)) {
                        resValues[i] = op.apply(v1.get(i), v2Values[i]);
                    } else {
                        resValues[i] = op.apply(0, v2Values[i]);
                    }
                }
            }
            res = new IntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else if (v1.isSparse() && v2.isSparse()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            IntDoubleVectorStorage newStorage = v1.getStorage().emptyDense();
            double[] resValues = newStorage.getValues();
            ObjectIterator<Int2DoubleMap.Entry> iter1 = v1.getStorage().entryIterator();
            while (iter1.hasNext()) {
                Int2DoubleMap.Entry entry = iter1.next();
                int idx = entry.getIntKey();
                resValues[idx] = entry.getDoubleValue();
            }
            if (v2.size() < Constant.denseLoopThreshold * v2.getDim()) {
                for (int i = 0; i < resValues.length; i++) {
                    resValues[i] = op.apply(resValues[i], 0);
                }
                ObjectIterator<Int2FloatMap.Entry> iter = v2.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2FloatMap.Entry entry = iter.next();
                    int idx = entry.getIntKey();
                    if (v1.getStorage().hasKey(idx)) {
                        resValues[idx] = op.apply(v1.get(idx), entry.getFloatValue());
                    }
                }
            } else {
                IntFloatVectorStorage v2Storage = v2.getStorage();
                for (int i = 0; i < resValues.length; i++) {
                    if (v2Storage.hasKey(i)) {
                        resValues[i] = op.apply(resValues[i], v2.get(i));
                    } else {
                        resValues[i] = op.apply(resValues[i], 0);
                    }
                }
            }
            res = new IntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else if (v1.isSparse() && v2.isSorted()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            IntDoubleVectorStorage newStorage = v1.getStorage().emptyDense();
            double[] resValues = newStorage.getValues();
            ObjectIterator<Int2DoubleMap.Entry> iter1 = v1.getStorage().entryIterator();
            while (iter1.hasNext()) {
                Int2DoubleMap.Entry entry = iter1.next();
                int idx = entry.getIntKey();
                resValues[idx] = entry.getDoubleValue();
            }
            if (v2.size() < Constant.denseLoopThreshold * v2.getDim()) {
                int[] v2Indices = v2.getStorage().getIndices();
                float[] v2Values = v2.getStorage().getValues();
                for (int i = 0; i < resValues.length; i++) {
                    resValues[i] = op.apply(resValues[i], 0);
                }
                int size = v2.size();
                for (int i = 0; i < size; i++) {
                    int idx = v2Indices[i];
                    if (v1.getStorage().hasKey(idx)) {
                        resValues[idx] = op.apply(v1.get(idx), v2Values[i]);
                    }
                }
            } else {
                IntFloatVectorStorage v2Storage = v2.getStorage();
                for (int i = 0; i < resValues.length; i++) {
                    if (v2Storage.hasKey(i)) {
                        resValues[i] = op.apply(resValues[i], v2.get(i));
                    } else {
                        resValues[i] = op.apply(resValues[i], 0);
                    }
                }
            }
            res = new IntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else if (v1.isSorted() && v2.isSparse()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            IntDoubleVectorStorage newStorage = v1.getStorage().emptyDense();
            double[] resValues = newStorage.getValues();
            int[] v1Indices = v1.getStorage().getIndices();
            double[] v1Values = v1.getStorage().getValues();
            int size = v1.size();
            for (int i = 0; i < size; i++) {
                int idx = v1Indices[i];
                resValues[idx] = v1Values[i];
            }
            if (v2.size() < Constant.denseLoopThreshold * v2.getDim()) {
                for (int i = 0; i < resValues.length; i++) {
                    resValues[i] = op.apply(resValues[i], 0);
                }
                ObjectIterator<Int2FloatMap.Entry> iter = v2.getStorage().entryIterator();
                while (iter.hasNext()) {
                    Int2FloatMap.Entry entry = iter.next();
                    int idx = entry.getIntKey();
                    if (v1.getStorage().hasKey(idx)) {
                        resValues[idx] = op.apply(v1.get(idx), entry.getFloatValue());
                    }
                }
            } else {
                IntFloatVectorStorage v2Storage = v2.getStorage();
                for (int i = 0; i < resValues.length; i++) {
                    if (v2Storage.hasKey(i)) {
                        resValues[i] = op.apply(resValues[i], v2.get(i));
                    } else {
                        resValues[i] = op.apply(resValues[i], 0);
                    }
                }
            }
            res = new IntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else if (v1.isSorted() && v2.isSorted()) {
        if (op.isKeepStorage()) {
            throw new AngelException("operation is not support!");
        } else {
            IntDoubleVectorStorage newStorage = v1.getStorage().emptyDense();
            double[] resValues = newStorage.getValues();
            int v1Pointor = 0;
            int v2Pointor = 0;
            int size1 = v1.size();
            int size2 = v2.size();
            int[] v1Indices = v1.getStorage().getIndices();
            double[] v1Values = v1.getStorage().getValues();
            int[] v2Indices = v2.getStorage().getIndices();
            float[] v2Values = v2.getStorage().getValues();
            if (!op.isCompare()) {
                for (int i = 0; i < resValues.length; i++) {
                    resValues[i] = Double.NaN;
                }
            }
            while (v1Pointor < size1 && v2Pointor < size2) {
                if (v1Indices[v1Pointor] == v2Indices[v2Pointor]) {
                    resValues[v1Indices[v1Pointor]] = op.apply(v1Values[v1Pointor], v2Values[v2Pointor]);
                    v1Pointor++;
                    v2Pointor++;
                } else if (v1Indices[v1Pointor] < v2Indices[v2Pointor]) {
                    resValues[v1Indices[v1Pointor]] = op.apply(v1Values[v1Pointor], 0);
                    v1Pointor++;
                } else {
                    // v1Indices[v1Pointor] > v2Indices[v2Pointor]
                    resValues[v2Indices[v2Pointor]] = op.apply(0, v2Values[v2Pointor]);
                    v2Pointor++;
                }
            }
            res = new IntDoubleVector(v1.getMatrixId(), v1.getRowId(), v1.getClock(), v1.getDim(), newStorage);
        }
    } else {
        throw new AngelException("The operation is not support!");
    }
    return res;
}
Also used : AngelException(com.tencent.angel.exception.AngelException) Int2DoubleMap(it.unimi.dsi.fastutil.ints.Int2DoubleMap) IntDoubleVector(com.tencent.angel.ml.math2.vector.IntDoubleVector) ObjectIterator(it.unimi.dsi.fastutil.objects.ObjectIterator) IntFloatVectorStorage(com.tencent.angel.ml.math2.storage.IntFloatVectorStorage) IntDoubleVectorStorage(com.tencent.angel.ml.math2.storage.IntDoubleVectorStorage) Int2FloatMap(it.unimi.dsi.fastutil.ints.Int2FloatMap)

Aggregations

IntFloatVector (com.tencent.angel.ml.math2.vector.IntFloatVector)104 ObjectIterator (it.unimi.dsi.fastutil.objects.ObjectIterator)60 Int2FloatMap (it.unimi.dsi.fastutil.ints.Int2FloatMap)57 IntFloatVectorStorage (com.tencent.angel.ml.math2.storage.IntFloatVectorStorage)50 CompIntFloatVector (com.tencent.angel.ml.math2.vector.CompIntFloatVector)34 IntDoubleVectorStorage (com.tencent.angel.ml.math2.storage.IntDoubleVectorStorage)33 IntIntVectorStorage (com.tencent.angel.ml.math2.storage.IntIntVectorStorage)32 IntLongVectorStorage (com.tencent.angel.ml.math2.storage.IntLongVectorStorage)32 IntFloatSparseVectorStorage (com.tencent.angel.ml.math2.storage.IntFloatSparseVectorStorage)31 LongDoubleVectorStorage (com.tencent.angel.ml.math2.storage.LongDoubleVectorStorage)30 LongFloatVectorStorage (com.tencent.angel.ml.math2.storage.LongFloatVectorStorage)30 LongIntVectorStorage (com.tencent.angel.ml.math2.storage.LongIntVectorStorage)30 LongLongVectorStorage (com.tencent.angel.ml.math2.storage.LongLongVectorStorage)30 Storage (com.tencent.angel.ml.math2.storage.Storage)30 IntFloatSortedVectorStorage (com.tencent.angel.ml.math2.storage.IntFloatSortedVectorStorage)25 IntDoubleSortedVectorStorage (com.tencent.angel.ml.math2.storage.IntDoubleSortedVectorStorage)21 AngelException (com.tencent.angel.exception.AngelException)20 IntDoubleSparseVectorStorage (com.tencent.angel.ml.math2.storage.IntDoubleSparseVectorStorage)20 IntIntSortedVectorStorage (com.tencent.angel.ml.math2.storage.IntIntSortedVectorStorage)20 IntIntSparseVectorStorage (com.tencent.angel.ml.math2.storage.IntIntSparseVectorStorage)20