Added New Distributed Functionalities Page

doc/source/distributed_functionalities.rst

@@ -0,0 +1,99 @@
+Distributed Functionalities as Numpy
+====================================
+
+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)

doc/source/index.rst

@@ -19,6 +19,7 @@ Heat is a distributed tensor framework for high performance data analytics.
    tutorials
    case_studies
    documentation_howto
+   distributed_functionalities
 
 Indices
 =======