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Nd4j refactoring (#111) 

* refactoring

Signed-off-by: Robert Altena <Rob@Ra-ai.com>

* wip

Signed-off-by: Robert Altena <Rob@Ra-ai.com>

* wip

Signed-off-by: Robert Altena <Rob@Ra-ai.com>
master
Robert Altena 2019-08-13 20:44:40 +09:00 committed by Alex Black
parent c29c011d1a
commit b10ab239c0
2 changed files with 44 additions and 51 deletions
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79
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java
View File

@ -2554,20 +2554,18 @@ public class Nd4j {
* @param dis the data input stream to read from
* @return the ndarray
*/
public static INDArray read(DataInputStream dis) throws IOException {
public static INDArray read(DataInputStream dis) {
val headerShape = BaseDataBuffer.readHeader(dis);
var shapeInformation = Nd4j.createBufferDetached(new long[]{headerShape.getMiddle().longValue()}, headerShape.getRight());
var shapeInformation = Nd4j.createBufferDetached(new long[]{headerShape.getMiddle()}, headerShape.getRight());
shapeInformation.read(dis, headerShape.getLeft(), headerShape.getMiddle(), headerShape.getThird());
val length = Shape.length(shapeInformation);
DataType type = null;
DataType type;
DataBuffer data = null;
val headerData = BaseDataBuffer.readHeader(dis);
try {
// current version contains dtype in extras
data = CompressedDataBuffer.readUnknown(dis, headerData.getFirst(), headerData.getMiddle(), headerData.getRight());
type = ArrayOptionsHelper.dataType(shapeInformation.asLong());
} catch (ND4JUnknownDataTypeException e) {
// manually setting data type
type = headerData.getRight();
@ -2767,23 +2765,9 @@ public class Nd4j {
}
public static INDArray appendBias(@NonNull INDArray... vectors) {
INDArray ret = INSTANCE.appendBias(vectors);
return ret;
return INSTANCE.appendBias(vectors);
}
/**
* Perform an operation along a diagonal
*
* @param x the ndarray to perform the operation on
* @param func the operation to perform
*/
public static void doAlongDiagonal(INDArray x, Function<Number, Number> func) {
if (x.isMatrix())
for (int i = 0; i < x.rows(); i++)
x.put(i, i, func.apply(x.getDouble(i, i)));
}
////////////////////// RANDOM ///////////////////////////////
/**
@ -2859,7 +2843,7 @@ public class Nd4j {
* @return the random ndarray with the specified shape
*/
public static INDArray rand(@NonNull DataType dataType, char order, @NonNull long... shape) {
INDArray ret = Nd4j.createUninitialized(dataType, shape, order); //INSTANCE.rand(order, shape);
INDArray ret = Nd4j.createUninitialized(dataType, shape, order);
return rand(ret);
}
@ -2874,7 +2858,7 @@ public class Nd4j {
* @return the random ndarray with the specified shape
*/
public static INDArray rand(@NonNull DataType dataType, @NonNull int... shape) {
INDArray ret = Nd4j.createUninitialized(dataType, ArrayUtil.toLongArray(shape), Nd4j.order()); //INSTANCE.rand(order, shape);
INDArray ret = Nd4j.createUninitialized(dataType, ArrayUtil.toLongArray(shape), Nd4j.order());
return rand(ret);
}
@ -2889,7 +2873,7 @@ public class Nd4j {
if (rows < 1 || columns < 1)
throw new ND4JIllegalStateException("Number of rows and columns should be positive for new INDArray");
INDArray ret = createUninitialized(new int[] {rows, columns}, Nd4j.order());//INSTANCE.rand(rows, columns, Nd4j.getRandom());
INDArray ret = createUninitialized(new int[] {rows, columns}, Nd4j.order());
return rand(ret);
}
@ -2928,7 +2912,6 @@ public class Nd4j {
*/
@Deprecated
public static INDArray rand(int[] shape, long seed) {
INDArray ret = createUninitialized(shape, Nd4j.order());//;INSTANCE.rand(shape, seed);
return rand(seed, ArrayUtil.toLongArray(shape));
}
@ -2992,8 +2975,6 @@ public class Nd4j {
* @return the random ndarray with the specified shape
*/
public static INDArray rand(@NonNull Distribution dist, @NonNull long... shape) {
//INDArray ret = INSTANCE.rand(shape, dist);
//logCreationIfNecessary(ret);
return dist.sample(shape);
}
@ -3006,7 +2987,7 @@ public class Nd4j {
* @return the random ndarray with the specified shape
*/
public static INDArray rand(int rows, int columns, @NonNull org.nd4j.linalg.api.rng.Random rng) {
INDArray ret = createUninitialized(new int[] {rows, columns}, order());//INSTANCE.rand(rows, columns, rng);
INDArray ret = createUninitialized(new int[] {rows, columns}, order());
return rand(ret, rng);
}
@ -3023,7 +3004,7 @@ public class Nd4j {
*/
@Deprecated
public static INDArray rand(long[] shape, double min, double max, @NonNull org.nd4j.linalg.api.rng.Random rng) {
INDArray ret = createUninitialized(shape, order()); //INSTANCE.rand(shape, min, max, rng);
INDArray ret = createUninitialized(shape, order());
return rand(ret, min, max, rng);
}
@ -3037,7 +3018,7 @@ public class Nd4j {
* @return a random matrix of the specified shape and range
*/
public static INDArray rand(double min, double max, @NonNull org.nd4j.linalg.api.rng.Random rng, @NonNull long... shape) {
INDArray ret = createUninitialized(shape, order()); //INSTANCE.rand(shape, min, max, rng);
INDArray ret = createUninitialized(shape, order());
return rand(ret, min, max, rng);
}
@ -3052,7 +3033,7 @@ public class Nd4j {
* @return a drandom matrix of the specified shape and range
*/
public static INDArray rand(int rows, int columns, double min, double max, @NonNull org.nd4j.linalg.api.rng.Random rng) {
INDArray ret = createUninitialized(rows, columns);//INSTANCE.rand(rows, columns, min, max, rng);
INDArray ret = createUninitialized(rows, columns);
return rand(ret, min, max, rng);
}
@ -3081,7 +3062,7 @@ public class Nd4j {
* Create a ndarray of the given shape and data type with values from N(0,1)
*
* @param shape the shape of the ndarray
* @return
* @return new array with random values
*/
public static INDArray randn(@NonNull DataType dataType, @NonNull int... shape) {
return randn(dataType, ArrayUtil.toLongArray(shape));
@ -3161,7 +3142,7 @@ public class Nd4j {
* Random normal N(0, 1) using the specified seed
*
* @param shape the shape of the array
* @return
* @return new array with random values
*/
public static INDArray randn(long seed, @NonNull long... shape) {
INDArray ret = Nd4j.createUninitialized(shape, order());
@ -3173,7 +3154,7 @@ public class Nd4j {
*
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @return
* @return new array with random values
*/
public static INDArray randn(long rows, long columns) {
INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
@ -3197,7 +3178,7 @@ public class Nd4j {
*
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @return
* @return new array with random values
*/
public static INDArray randn(long rows, long columns, long seed) {
INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
@ -3210,7 +3191,7 @@ public class Nd4j {
* @param rows the number of rows in the matrix
* @param columns the number of columns in the matrix
* @param r the random generator to use
* @return
* @return new array with random values
*/
public static INDArray randn(long rows, long columns, @NonNull org.nd4j.linalg.api.rng.Random r) {
INDArray ret = Nd4j.createUninitialized(new long[]{rows, columns}, order());
@ -3238,7 +3219,7 @@ public class Nd4j {
*
* @param shape the shape of the array
* @param r the random generator to use
* @return
* @return new array with random values
*/
public static INDArray randn(@NonNull org.nd4j.linalg.api.rng.Random r, @NonNull long... shape) {
final INDArray ret = Nd4j.createUninitialized(shape, order());
@ -3409,9 +3390,9 @@ public class Nd4j {
*
* PLEASE NOTE: memory of underlying array will be NOT initialized, and won't be set to 0.0
*
* @param rows
* @param columns
* @return
* @param rows rows
* @param columns columns
* @return uninitialized 2D array of rows x columns
*/
public static INDArray createUninitialized(long rows, long columns) {
return createUninitialized(new long[] {rows, columns});
@ -3532,7 +3513,7 @@ public class Nd4j {
* @return the created ndarray.
*/
public static INDArray create(double[][][] data) {
return create(ArrayUtil.flatten(data), new int[] {data.length, data[0].length, data[0][0].length});
return create(ArrayUtil.flatten(data), data.length, data[0].length, data[0][0].length);
}
/**
@ -3541,7 +3522,7 @@ public class Nd4j {
* @return the created ndarray.
*/
public static INDArray create(float[][][] data) {
return create(ArrayUtil.flatten(data), new int[] {data.length, data[0].length, data[0][0].length});
return create(ArrayUtil.flatten(data), data.length, data[0].length, data[0][0].length);
}
/**
@ -3559,7 +3540,7 @@ public class Nd4j {
* @return the created ndarray.
*/
public static INDArray create(double[][][][] data) {
return create(ArrayUtil.flatten(data), new int[] {data.length, data[0].length, data[0][0].length, data[0][0][0].length});
return create(ArrayUtil.flatten(data), data.length, data[0].length, data[0][0].length, data[0][0][0].length);
}
/**
@ -3568,7 +3549,7 @@ public class Nd4j {
* @return the created ndarray.
*/
public static INDArray create(float[][][][] data) {
return create(ArrayUtil.flatten(data), new int[] {data.length, data[0].length, data[0][0].length, data[0][0][0].length});
return create(ArrayUtil.flatten(data), data.length, data[0].length, data[0][0].length, data[0][0][0].length);
}
/**
@ -3609,8 +3590,7 @@ public class Nd4j {
* @return the created ndarray
*/
public static INDArray create(float[] data, char order) {
INDArray ret = INSTANCE.create(data, order);
return ret;
return INSTANCE.create(data, order);
}
/**
@ -3621,8 +3601,7 @@ public class Nd4j {
* @return the created ndarray
*/
public static INDArray create(double[] data, char order) {
INDArray ret = INSTANCE.create(data, order);
return ret;
return INSTANCE.create(data, order);
}
/**
@ -3633,8 +3612,7 @@ public class Nd4j {
* @return the created ndarray
*/
public static INDArray create(int columns, char order) {
INDArray ret = INSTANCE.create(new long[] {columns}, Nd4j.getStrides(new long[] {columns}, order), 0, order);
return ret;
return INSTANCE.create(new long[] {columns}, Nd4j.getStrides(new long[] {columns}, order), 0, order);
}
/**
@ -3719,8 +3697,7 @@ public class Nd4j {
* @return the created ndarray.
*/
public static INDArray create(int[] data, long[] shape, long[]strides, char order, DataType type) {
val ret = INSTANCE.create(data, shape, strides, order, type, Nd4j.getMemoryManager().getCurrentWorkspace());
return ret;
return INSTANCE.create(data, shape, strides, order, type, Nd4j.getMemoryManager().getCurrentWorkspace());
}
/**

16
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/rng/RngTests.java
View File

@ -101,6 +101,22 @@ public class RngTests extends BaseNd4jTest {
}
@Test
void testRandomBinomial() {
//silly tests. Just increasing the usage for randomBinomial to stop compiler warnings.
INDArray x = Nd4j.randomBinomial(10, 0.5, 3,3);
assertTrue(x.sum().getDouble(0) > 0.0); //silly test. Just increasing th usage for randomBinomial
x = Nd4j.randomBinomial(10, 0.5, x);
assertTrue(x.sum().getDouble(0) > 0.0);
x = Nd4j.randomExponential(0.5, 3,3);
assertTrue(x.sum().getDouble(0) > 0.0);
x = Nd4j.randomExponential(0.5, x);
assertTrue(x.sum().getDouble(0) > 0.0);
}
@Override
public char ordering() {