Proxem.NumNet
1.5.0
C# scientific package containing among other things :
* an Ndimensionnal array object and the main functions to operate on it
* the main linear algebra functions on ndimensionnal arrays.
The synthax is mainly based on python's numpy library. MKL is used for optimized performances.
See the version list below for details.
InstallPackage Proxem.NumNet Version 1.5.0
dotnet add package Proxem.NumNet version 1.5.0
<PackageReference Include="Proxem.NumNet" Version="1.5.0" />
paket add Proxem.NumNet version 1.5.0
NumNet
NumNet is an optimized library for matrix operations and scientific programming written in C# and developped at Proxem.
NumNet is inspired by python's numpy library to facilitate its use by python developpers.
Table of contents
Requirements
NumNet was developped in .Net Standard 2.0 and is compatible with both .Net Framwork and .Net Core thus working on Windows and Linux platform.
For Mac OS users there shouldn't be any problem but we didn't test extensively.
NumNet relies on BlasNet for the low level operations on arrays.
See BlasNet documentation for further informations on how to use Intel's MKL for low level operations.
Examples
Matrix creations
To create an empty 2dimensional array of dimension 3 and 4 you can use
var zeroArray = NN.Zeros(3, 4);
The follwing creates a 1dimensional array with all even number from 0 to 40
var range = NN.Range(0, 40, step: 2);
To reshape the previous array to a 2d dimension array use var 2dRange = range.Reshape(4, 5);
.
This operation will be a O(1) if possible but it might need to copy the values
(if the initial matrix is transposed or more generaly if the data in the initial array are not contiguous.)
Random initializations are also supported, here are a few examples of the supported distributions
var bern = NN.Random.Bernouilli(0.5, 2, 3); // 2 x 3 matrix
var norm = NN.Random.Normal(0, 1, 10, 10); // 10 x 10 normally distributed matrix
var unif = NN.Random.Uniform(1, 1, 5, 6); // 5 x 6 uniform matrix between 1 and 1
Accessing values
Let's start with a 2d array of size (5 x 6)
var M = NN.Range(30).Reshape(5, 6);
To access a single value in the array we will use M[i, j]
.
NumNet also supports more complex slicing functions.
To select the first column of the array we will use
var vector = M[Slicer._, 0]; // 'Slicer._' correspond to ':' in numpy
More control on the slices is made using the following
var v0 = M[0]; // [0, 1, 2, 3, 4, 5]
var v1 = M[Slicer.Range(0, 3), Slicer.Until(2)]; // [[0, 1],[6, 7],[12, 13]]
var v2 = M[1, Slicer.From(1)]; // [7, 8, 9, 10, 11]
var v3 = M[Slicer.Range(3, 1), 2]; // [16, 22]
Base operations
The synthax for operations between multidimensional arrays is mostly the same as numpy (with Pascal Case).
For instance, matrix multiplications will be done with the follwing code
var M = NN.Random.Normal(0, 1, 3, 4);
var N = NN.Random.Bernouilli(0.6, 4, 5);
var MN = NN.Dot(M, N) // gives a (3 x 5) matrix
var MMNTranspose = NN.Dot(MN.T, M) // gives a (5 x 4) matrix
where MN.T
stands for the transpose of MN
.
Github
The code of NumNet is opensource, you can find it on our github.
Contact
If you can't make NumNet work on your computer or if you have any tracks of improvement drop us an email at one of the following address:
 thp@proxem.com
 joc@proxem.com
License
NumNet is Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.
See the NOTICE file distributed with this work for additional information regarding copyright ownership.
The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.
NumNet
NumNet is an optimized library for matrix operations and scientific programming written in C# and developped at Proxem.
NumNet is inspired by python's numpy library to facilitate its use by python developpers.
Table of contents
Requirements
NumNet was developped in .Net Standard 2.0 and is compatible with both .Net Framwork and .Net Core thus working on Windows and Linux platform.
For Mac OS users there shouldn't be any problem but we didn't test extensively.
NumNet relies on BlasNet for the low level operations on arrays.
See BlasNet documentation for further informations on how to use Intel's MKL for low level operations.
Examples
Matrix creations
To create an empty 2dimensional array of dimension 3 and 4 you can use
var zeroArray = NN.Zeros(3, 4);
The follwing creates a 1dimensional array with all even number from 0 to 40
var range = NN.Range(0, 40, step: 2);
To reshape the previous array to a 2d dimension array use var 2dRange = range.Reshape(4, 5);
.
This operation will be a O(1) if possible but it might need to copy the values
(if the initial matrix is transposed or more generaly if the data in the initial array are not contiguous.)
Random initializations are also supported, here are a few examples of the supported distributions
var bern = NN.Random.Bernouilli(0.5, 2, 3); // 2 x 3 matrix
var norm = NN.Random.Normal(0, 1, 10, 10); // 10 x 10 normally distributed matrix
var unif = NN.Random.Uniform(1, 1, 5, 6); // 5 x 6 uniform matrix between 1 and 1
Accessing values
Let's start with a 2d array of size (5 x 6)
var M = NN.Range(30).Reshape(5, 6);
To access a single value in the array we will use M[i, j]
.
NumNet also supports more complex slicing functions.
To select the first column of the array we will use
var vector = M[Slicer._, 0]; // 'Slicer._' correspond to ':' in numpy
More control on the slices is made using the following
var v0 = M[0]; // [0, 1, 2, 3, 4, 5]
var v1 = M[Slicer.Range(0, 3), Slicer.Until(2)]; // [[0, 1],[6, 7],[12, 13]]
var v2 = M[1, Slicer.From(1)]; // [7, 8, 9, 10, 11]
var v3 = M[Slicer.Range(3, 1), 2]; // [16, 22]
Base operations
The synthax for operations between multidimensional arrays is mostly the same as numpy (with Pascal Case).
For instance, matrix multiplications will be done with the follwing code
var M = NN.Random.Normal(0, 1, 3, 4);
var N = NN.Random.Bernouilli(0.6, 4, 5);
var MN = NN.Dot(M, N) // gives a (3 x 5) matrix
var MMNTranspose = NN.Dot(MN.T, M) // gives a (5 x 4) matrix
where MN.T
stands for the transpose of MN
.
Github
The code of NumNet is opensource, you can find it on our github.
Contact
If you can't make NumNet work on your computer or if you have any tracks of improvement drop us an email at one of the following address:
 thp@proxem.com
 joc@proxem.com
License
NumNet is Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.
See the NOTICE file distributed with this work for additional information regarding copyright ownership.
The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and limitations under the License.
Dependencies

 Proxem.BlasNet (>= 1.5.0)
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