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API reference improvements #1111

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API reference improvements #1111

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Added New Distributed Functionalities Page
AsRaNi1 Feb 15, 2023
12f3ad4
modified page for distributed functionalities
AsRaNi1 Feb 15, 2023
bb0ab11
modified page for distributed functionalities
AsRaNi1 Feb 15, 2023
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modified page for distributed functionalities
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AsRaNi1 committed Feb 15, 2023
commit bb0ab115bae3e71adccf36a7b713ed623c15b998
93 changes: 0 additions & 93 deletions doc/source/distributed_functionalities.rst
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Expand Up @@ -5,99 +5,6 @@ Inference
---------
Heat is a Python package for accelerated and distributed tensor computations. Internally, it is based on PyTorch. Heat has various functionalities similar to that of numpy/scipy and scikit-learn. What more is offered is that it supports distributed tensor computations for accelerated computations. The implementations allows us to tackle use cases that would otherwise exceed memory limits of a single node. Below listed are the various operatiosn that are performed on heat, just like any other tensor library:

.. Features
.. --------

.. Importing Heat:

.. .. code:: python

.. import heat as ht

.. Array Initialization:

.. .. code:: python

.. ht.array([[1, 2, 3],
.. [4, 5, 6],
.. [7, 8, 9]], split=1)

.. Basic Numpy operations performed in Heat:

.. .. code:: python

.. #Various Array initilization menthods
.. ht.zeros((3, 4))
.. ht.ones((3, 4))
.. ht.random.randn(3)
.. ht.linspace(3, 8)
.. ht.arange(10)
.. ht.full(3, 9)
.. ht.eye(4)


.. #Basic Mathematical Functions
.. ht.sin(ht.array([1, 2, 3]))
.. ht.cos(ht.array([3, 4, 5]))
.. ht.log(ht.array([5, 6, 7]))
.. ht.exp(ht.array([7, 8, 9]))
.. ht.sqrt(ht.array([9, 1, 2]))
.. ht.min(ht.array([4, 8, 9]))
.. ht.max(ht.array([1, 1, 7]))
.. ht.unique(ht.array([3, 6, 6]))
.. ht.mean(ht.array([9, 1, 2]))
.. ht.median(ht.array([5, 1, 5]))



.. Matrix Operaterations:

.. .. code:: python

.. a = ht.array([1, 2, 3])
.. b = ht.array([4, 5, 6])

.. #Matrix Multiplication
.. ht.matmul(a, b, True)
.. #boolean expression represents whether to distribute a in the case that both a.split is None and b.split is None

.. #Matrix Norm
.. ht.matrix_norm(ht.array([[1, 2], [3, 4]]))

.. #Transpose of a matrix
.. ht.transpose(a)

.. #Dot product
.. ht.dot(a, b)

.. #Cross Product
.. ht.cross(a, b)

.. #Reshape a Matrix
.. ht.reshape(a, (3, 1))


.. Data science and Machine Learning
.. ---------------------------------

.. .. code:: python

.. X = ht.random.randn(10, 4, split=0)
.. Y = ht.random.randn(10, 1, split=0)

.. #Linear Regression (a linear model with L1 regularization)
.. e = ht.regression.lasso.Lasso(max_iter=10)
.. e.fit(X, Y)

.. #K nearest neighbour
.. knn = ht.classification.kneighborsclassifier.KNeighborsClassifier(n_neighbors=1)
.. knn.fit(X, Y)

.. #Naive Bayes
.. #Gaussian Naive Bayes
.. clf=ht.naive_bayes.GaussianNB()
.. clf.fit(X, Y)

Summary Table
-------------

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