From 0eefe006fadf5646c0be4c8283743ae6051ea5c0 Mon Sep 17 00:00:00 2001
From: Yogesh Vishwakarma <103316955+Yogeshkarma@users.noreply.github.com>
Date: Tue, 4 Jun 2024 20:07:56 +0530
Subject: [PATCH] Delete contrib/numpy/Universal-Functions.md

for creating two PR separately
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-# Universal functions (ufunc)
-
----
-
-A `ufunc`, short for "`universal function`," is a fundamental concept in NumPy, a powerful library for numerical computing in Python. Universal functions are highly optimized, element-wise functions designed to perform operations on data stored in NumPy arrays. 
-
-
-
-## Uses of Ufuncs in NumPy
-
-Universal functions (ufuncs) in NumPy provide a wide range of functionalities for efficient and powerful numerical computations. Below is a detailed explanation of their uses:
-
-### 1. **Element-wise Operations**
-Ufuncs perform operations on each element of the arrays independently.
-
-```python
-import numpy as np
-
-A = np.array([1, 2, 3, 4])
-B = np.array([5, 6, 7, 8])
-
-# Element-wise addition
-np.add(A, B)  # Output: array([ 6,  8, 10, 12])
-```
-
-### 2. **Broadcasting**
-Ufuncs support broadcasting, allowing operations on arrays with different shapes, making it possible to perform operations without explicitly reshaping arrays.
-
-```python
-C = np.array([1, 2, 3])
-D = np.array([[1], [2], [3]])
-
-# Broadcasting addition
-np.add(C, D)  # Output: array([[2, 3, 4], [3, 4, 5], [4, 5, 6]])
-```
-
-### 3. **Vectorization**
-Ufuncs are vectorized, meaning they are implemented in low-level C code, allowing for fast execution and avoiding the overhead of Python loops.
-
-```python
-# Vectorized square root
-np.sqrt(A)  # Output: array([1., 1.41421356, 1.73205081, 2.])
-```
-
-### 4. **Type Flexibility**
-Ufuncs handle various data types and perform automatic type casting as needed.
-
-```python
-E = np.array([1.0, 2.0, 3.0])
-F = np.array([4, 5, 6])
-
-# Addition with type casting
-np.add(E, F)  # Output: array([5., 7., 9.])
-```
-
-### 5. **Reduction Operations**
-Ufuncs support reduction operations, such as summing all elements of an array or finding the product of all elements.
-
-```python
-# Summing all elements
-np.add.reduce(A)  # Output: 10
-
-# Product of all elements
-np.multiply.reduce(A)  # Output: 24
-```
-
-### 6. **Accumulation Operations**
-Ufuncs can perform accumulation operations, which keep a running tally of the computation.
-
-```python
-# Cumulative sum
-np.add.accumulate(A)  # Output: array([ 1,  3,  6, 10])
-```
-
-### 7. **Reduceat Operations**
-Ufuncs can perform segmented reductions using the `reduceat` method, which applies the ufunc at specified intervals.
-
-```python
-G = np.array([0, 1, 2, 3, 4, 5, 6, 7])
-indices = [0, 2, 5]
-np.add.reduceat(G, indices)  # Output: array([ 1,  9, 18])
-```
-
-### 8. **Outer Product**
-Ufuncs can compute the outer product of two arrays, producing a matrix where each element is the result of applying the ufunc to each pair of elements from the input arrays.
-
-```python
-# Outer product
-np.multiply.outer([1, 2, 3], [4, 5, 6])  
-# Output: array([[ 4,  5,  6],
-#                [ 8, 10, 12],
-#                [12, 15, 18]])
-```
-
-### 9. **Out Parameter**
-Ufuncs can use the `out` parameter to store results in a pre-allocated array, saving memory and improving performance.
-
-```python
-result = np.empty_like(A)
-np.multiply(A, B, out=result)  # Output: array([ 5, 12, 21, 32])
-```
-
-# Create Your Own Ufunc
-
-You can create custom ufuncs for specific needs using np.frompyfunc or np.vectorize, allowing Python functions to behave like ufuncs.
-
-Here, we are using `frompyfunc()` which takes three argument:
-
-1. function - the name of the function.
-2. inputs - the number of input  (arrays).
-3. outputs - the number of output arrays.
-
-```python
-def my_add(x, y):
-    return x + y
-
-my_add_ufunc = np.frompyfunc(my_add, 2, 1)
-my_add_ufunc(A, B)  # Output: array([ 6,  8, 10, 12], dtype=object)
-```
-# Some Common Ufunc are
-
-Here are some commonly used ufuncs in NumPy:
-
-- **Arithmetic**: `np.add`, `np.subtract`, `np.multiply`, `np.divide`
-- **Trigonometric**: `np.sin`, `np.cos`, `np.tan`
-- **Exponential and Logarithmic**: `np.exp`, `np.log`, `np.log10`
-- **Comparison**: `np.maximum`, `np.minimum`, `np.greater`, `np.less`
-- **Logical**: `np.logical_and`, `np.logical_or`, `np.logical_not`
-
-For more such Ufunc, address to  [Universal functions (ufunc) — NumPy](https://numpy.org/doc/stable/reference/ufuncs.html)
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