Index

A B C D E F G H I L M N O P R S T V Z
All Classes and Interfaces|All Packages

A

ActivationFunction - Interface in org.numpy4j.activations

Represents an activation function applied to an NDArray.

Activations - Class in org.numpy4j.activations

Collection of common activation functions used in neural networks.

add(NDArray) - Method in class org.numpy4j.core.NDArray

Adds another NDArray to this one element-wise, applying NumPy-style broadcasting if necessary.

add(NDArray, NDArray) - Method in interface org.numpy4j.backend.ArrayBackend

add(NDArray, NDArray) - Method in class org.numpy4j.backend.CpuBackend

apply(NDArray) - Method in interface org.numpy4j.activations.ActivationFunction

Applies the activation function to the given NDArray.

arange(double, double, double) - Static method in class org.numpy4j.api.Numpy

Creates a one-dimensional NDArray containing evenly spaced values within a specified interval.

array(double[], int...) - Static method in class org.numpy4j.api.Numpy

Creates a new NDArray from an existing one-dimensional Java array and a specified shape.

ArrayBackend - Interface in org.numpy4j.backend

ArrayStorage - Interface in org.numpy4j.storage

B

BackendFactory - Class in org.numpy4j.backend

Factory for obtaining the numerical backend associated with a device.

C

close() - Method in interface org.numpy4j.storage.ArrayStorage

Releases resources owned by this storage.

close() - Method in class org.numpy4j.storage.CpuStorage

convolve(NDArray) - Method in class org.numpy4j.core.NDArray

Computes the one-dimensional discrete convolution of this array with another one-dimensional array.

copy(NDArray) - Static method in class org.numpy4j.api.Numpy

Creates a deep copy of the given NDArray.

copyFromHost(double[]) - Method in interface org.numpy4j.storage.ArrayStorage

Copies values from a host-side array into this storage.

copyFromHost(double[]) - Method in class org.numpy4j.storage.CpuStorage

copyToHost() - Method in interface org.numpy4j.storage.ArrayStorage

Copies the stored values into a host-side Java array.

copyToHost() - Method in class org.numpy4j.storage.CpuStorage

cpu() - Static method in class org.numpy4j.device.Device

CPU - Enum constant in enum class org.numpy4j.device.DeviceType

CpuBackend - Class in org.numpy4j.backend

CpuBackend() - Constructor for class org.numpy4j.backend.CpuBackend

CpuStorage - Class in org.numpy4j.storage

CPU-based implementation of ArrayStorage.

CpuStorage(double[]) - Constructor for class org.numpy4j.storage.CpuStorage

Creates CPU storage by copying the supplied values.

CpuStorage(int) - Constructor for class org.numpy4j.storage.CpuStorage

Creates zero-initialized CPU storage.

createStorage(Device, int) - Static method in class org.numpy4j.device.DeviceManager

cuda(int) - Static method in class org.numpy4j.device.Device

CUDA - Enum constant in enum class org.numpy4j.device.DeviceType

D

det(NDArray) - Static method in class org.numpy4j.linalg.LinearAlgebra

Computes the determinant of a square matrix using recursive Laplace expansion.

Device - Class in org.numpy4j.device

Represents a computing device available to numpy4j.

Device(DeviceType, int) - Constructor for class org.numpy4j.device.Device

DeviceManager - Class in org.numpy4j.device

DeviceType - Enum Class in org.numpy4j.device

divide(NDArray) - Method in class org.numpy4j.core.NDArray

Divides this NDArray by another element-wise, applying NumPy-style broadcasting if necessary.

divide(NDArray, NDArray) - Method in interface org.numpy4j.backend.ArrayBackend

divide(NDArray, NDArray) - Method in class org.numpy4j.backend.CpuBackend

dot(NDArray) - Method in class org.numpy4j.core.NDArray

Computes the matrix dot product between this array and another.

dot(NDArray, NDArray) - Method in interface org.numpy4j.backend.ArrayBackend

dot(NDArray, NDArray) - Method in class org.numpy4j.backend.CpuBackend

dot(NDArray, NDArray) - Static method in class org.numpy4j.linalg.LinearAlgebra

Computes the dot product (inner product) of two 1D vectors.

E

empty(int...) - Static method in class org.numpy4j.api.Numpy

Creates a new NDArray of the specified shape with uninitialized (random) values.

equals(Object) - Method in class org.numpy4j.device.Device

exp(NDArray) - Static method in class org.numpy4j.api.Numpy

Element-wise exponential of all elements in the input NDArray.

eye(int) - Static method in class org.numpy4j.api.Numpy

Creates a two-dimensional identity matrix of size n × n.

F

fft(double[], double[]) - Static method in class org.numpy4j.fft.FFT

FFT - Class in org.numpy4j.fft

FFT() - Constructor for class org.numpy4j.fft.FFT

fit(NDArray, NDArray, int, double) - Method in class org.numpy4j.ml.LogisticRegression

Trains the model using batch gradient descent.

flatten(NDArray) - Static method in class org.numpy4j.api.Numpy

Flattens the input NDArray to a one-dimensional array.

full(double, int...) - Static method in class org.numpy4j.api.Numpy

Creates a new NDArray of the specified shape filled with a given constant value.

G

get(int) - Method in interface org.numpy4j.storage.ArrayStorage

get(int) - Method in class org.numpy4j.storage.CpuStorage

get(int...) - Method in class org.numpy4j.core.NDArray

Returns the element at the specified indices.

get(Device) - Static method in class org.numpy4j.backend.BackendFactory

getAvailableDevices() - Static method in class org.numpy4j.device.DeviceManager

Returns the devices currently supported by this implementation.

getBias() - Method in class org.numpy4j.ml.LogisticRegression

Returns the bias term.

getData() - Method in class org.numpy4j.core.NDArray

Returns the data (dimensions) of this NDArray.

getDefaultDevice() - Static method in class org.numpy4j.device.DeviceManager

Returns the default device.

getDevice() - Method in class org.numpy4j.core.NDArray

getDevice() - Method in interface org.numpy4j.storage.ArrayStorage

Returns the device that owns this storage.

getDevice() - Method in class org.numpy4j.storage.CpuStorage

getIndex() - Method in class org.numpy4j.device.Device

getNdims() - Method in class org.numpy4j.core.NDArray

Returns the number of dimensions (like NumPy's ndarray.ndim)

getRow(int) - Method in class org.numpy4j.core.NDArray

getShape() - Method in class org.numpy4j.core.NDArray

Returns the shape (dimensions) of this NDArray.

getSize() - Method in class org.numpy4j.core.NDArray

Returns the number of elements in the array (like NumPy's ndarray.size)

getType() - Method in class org.numpy4j.device.Device

getWeights() - Method in class org.numpy4j.ml.LogisticRegression

Returns the learned weights.

H

hashCode() - Method in class org.numpy4j.device.Device

I

inv(NDArray) - Static method in class org.numpy4j.linalg.LinearAlgebra

Computes the inverse of a square matrix using Gaussian elimination.

isAvailable(Device) - Static method in class org.numpy4j.device.DeviceManager

Checks whether the specified device is available.

L

like(double[]) - Method in class org.numpy4j.core.NDArray

Creates a new NDArray with the same structural metadata (shape, layout) as this array, backed by the provided data buffer.

LinearAlgebra - Class in org.numpy4j.linalg

A utility class providing core linear algebra operations on NDArray objects, including vector and matrix computations such as dot products, matrix multiplication, transposition, norms, determinants, inverses, and identity matrix creation.

LinearAlgebra() - Constructor for class org.numpy4j.linalg.LinearAlgebra

linspace(double, double, int) - Static method in class org.numpy4j.api.Numpy

Generates a one-dimensional NDArray of evenly spaced numbers over a specified interval.

LogisticRegression - Class in org.numpy4j.ml

Binary Logistic Regression implemented using numpy4j NDArray.

LogisticRegression(int) - Constructor for class org.numpy4j.ml.LogisticRegression

Creates a Logistic Regression model.

M

map(DoubleUnaryOperator) - Method in class org.numpy4j.core.NDArray

Applies the given unary function to each element of this NDArray.

matmul(NDArray, NDArray) - Static method in class org.numpy4j.linalg.LinearAlgebra

Performs matrix multiplication between two 2D matrices:

mean() - Method in class org.numpy4j.core.NDArray

Computes the mean (average) of all elements in the NDArray.

mean(NDArray) - Static method in class org.numpy4j.api.Numpy

Compute the mean (average) of all elements in the input NDArray.

multiply(NDArray) - Method in class org.numpy4j.core.NDArray

Multiplies this NDArray with another element-wise, applying NumPy-style broadcasting if necessary.

multiply(NDArray, NDArray) - Method in interface org.numpy4j.backend.ArrayBackend

multiply(NDArray, NDArray) - Method in class org.numpy4j.backend.CpuBackend

N

NDArray - Class in org.numpy4j.core

A lightweight, high-performance Java implementation of a multidimensional array, inspired by Python's NumPy ndarray.

NDArray(double[], int...) - Constructor for class org.numpy4j.core.NDArray

NDArray(int...) - Constructor for class org.numpy4j.core.NDArray

NDArray(Device, int...) - Constructor for class org.numpy4j.core.NDArray

nextGaussian() - Static method in class org.numpy4j.random.RandomUtils

nextUniform(double, double) - Static method in class org.numpy4j.random.RandomUtils

norm(NDArray) - Static method in class org.numpy4j.linalg.LinearAlgebra

Computes the Euclidean (L2) norm of a vector:

Numpy - Class in org.numpy4j.api

Numpy() - Constructor for class org.numpy4j.api.Numpy

O

of(double[], int...) - Static method in class org.numpy4j.core.NDArray

ones(int...) - Static method in class org.numpy4j.api.Numpy

Creates a new NDArray of the specified shape filled with ones.

org.numpy4j.activations - package org.numpy4j.activations

org.numpy4j.api - package org.numpy4j.api

org.numpy4j.backend - package org.numpy4j.backend

org.numpy4j.core - package org.numpy4j.core

org.numpy4j.device - package org.numpy4j.device

org.numpy4j.fft - package org.numpy4j.fft

org.numpy4j.linalg - package org.numpy4j.linalg

org.numpy4j.ml - package org.numpy4j.ml

org.numpy4j.random - package org.numpy4j.random

org.numpy4j.storage - package org.numpy4j.storage

P

power(int) - Method in class org.numpy4j.core.NDArray

Raises each element to the given power.

predict(NDArray) - Method in class org.numpy4j.ml.LogisticRegression

Predicts binary class labels using a threshold of 0.5.

predictProba(NDArray) - Method in class org.numpy4j.ml.LogisticRegression

Computes predicted probabilities using the sigmoid function.

R

random(int...) - Static method in class org.numpy4j.api.Numpy

Generate an NDArray of random values uniformly distributed in [0, 1).

random(int...) - Static method in class org.numpy4j.core.NDArray

Creates an NDArray with the given shape, filled with random values uniformly distributed between 0 (inclusive) and 1 (exclusive).

RandomUtils - Class in org.numpy4j.random

RandomUtils() - Constructor for class org.numpy4j.random.RandomUtils

rank(NDArray) - Static method in class org.numpy4j.linalg.LinearAlgebra

Computes the rank of a matrix.

ravel(NDArray) - Static method in class org.numpy4j.api.Numpy

Alias for Numpy.flatten(NDArray).

relu(NDArray) - Static method in class org.numpy4j.activations.Activations

Applies the Rectified Linear Unit (ReLU) activation function.

reshape(int...) - Method in class org.numpy4j.core.NDArray

Returns a new NDArray with the same data but a different shape.

reshape(NDArray, int...) - Static method in class org.numpy4j.api.Numpy

Reshapes the input NDArray to the specified new shape.

S

set(double, int...) - Method in class org.numpy4j.core.NDArray

Sets the element at the specified indices to the given value.

set(int, double) - Method in interface org.numpy4j.storage.ArrayStorage

set(int, double) - Method in class org.numpy4j.storage.CpuStorage

sigmoid(NDArray) - Static method in class org.numpy4j.activations.Activations

Applies the sigmoid (logistic) activation function.

size() - Method in interface org.numpy4j.storage.ArrayStorage

Returns the number of stored elements.

size() - Method in class org.numpy4j.storage.CpuStorage

slice(int[][]) - Method in class org.numpy4j.core.NDArray

Extracts a subarray (slice) from this NDArray, similar to numpy[:, :] slicing in Python.

softmax(NDArray) - Static method in class org.numpy4j.activations.Activations

Applies the softmax activation function.

solve(NDArray, NDArray) - Static method in class org.numpy4j.linalg.LinearAlgebra

Solves a system of linear equations A · x = b.

subtract(NDArray) - Method in class org.numpy4j.core.NDArray

Subtracts another NDArray from this one element-wise, applying NumPy-style broadcasting if necessary.

subtract(NDArray, NDArray) - Method in interface org.numpy4j.backend.ArrayBackend

subtract(NDArray, NDArray) - Method in class org.numpy4j.backend.CpuBackend

sum() - Method in class org.numpy4j.core.NDArray

Computes the sum of all elements in the NDArray.

T

tanh(NDArray) - Static method in class org.numpy4j.activations.Activations

Applies the hyperbolic tangent (tanh) activation function.

to(Device) - Method in class org.numpy4j.core.NDArray

toString() - Method in class org.numpy4j.core.NDArray

toString() - Method in class org.numpy4j.device.Device

transpose(NDArray) - Static method in class org.numpy4j.linalg.LinearAlgebra

Returns the transpose of a 2D matrix.

V

valueOf(String) - Static method in enum class org.numpy4j.device.DeviceType

Returns the enum constant of this class with the specified name.

values() - Static method in enum class org.numpy4j.device.DeviceType

Returns an array containing the constants of this enum class, in the order they are declared.

Z

zeros(int...) - Static method in class org.numpy4j.api.Numpy

Creates a new NDArray of the specified shape filled with zeros.

zeros(int...) - Static method in class org.numpy4j.core.NDArray

Creates an NDArray with the given shape, filled with zeros.

A B C D E F G H I L M N O P R S T V Z
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