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Explain Operator Overloading in python with example
Operator overloading :
Everything in Python is an object. Each object has some special internal methods which it uses to interact with other
objects. Generally, these methods follow the __action__ naming convention. Collectively, this is termed as the Python Data Model.
You can overload any of these methods. This is commonly used in operator overloading in Python.
Python operators work for built-in classes. But the same operator behaves differently with different types. For example, the + operator will perform arithmetic addition on two numbers, merge two lists, or concatenate two strings.
This feature in Python that allows the same operator to have different meaning according to the context is called operator overloading.
Below is an example of operator overloading using Python's data model. The Vector class creates a simple vector of two
variables. We'll add appropriate support for mathematical operations of two vectors using operator overloading.
class Vector(object): def __init__(self, x, y): self.x = x self.y = y def __add__(self, v): # Addition with another vector. return Vector(self.x + v.x, self.y + v.y) def __sub__(self, v): # Subtraction with another vector. return Vector(self.x - v.x, self.y - v.y) def __mul__(self, s): # Multiplication with a scalar. return Vector(self.x * s, self.y * s) def __div__(self, s): # Division with a scalar. float_s = float(s) return Vector(self.x / float_s, self.y / float_s) def __floordiv__(self, s): # Division with a scalar (value floored). return Vector(self.x // s, self.y // s) def __repr__(self): # Print friendly representation of Vector class. Else, it would # show up like, <__main__.Vector instance at 0x01DDDDC8>. return '<Vector (%f, %f)>' % (self.x, self.y, ) a = Vector(3, 5) b = Vector(2, 7) print a + b # Output: <Vector (5.000000, 12.000000)> print b - a # Output: <Vector (-1.000000, 2.000000)> print b * 1.3 # Output: <Vector (2.600000, 9.100000)> print a // 17 # Output: <Vector (0.000000, 0.000000)> print a / 17 # Output: <Vector (0.176471, 0.294118)>
Running random forest algorithm with one variable