What is the shortest Python code to get a maximum from the two discrete functions?

program to find maximum and minimum in python
find max and min in a list python
find max and min in a list python without inbuilt function
python statistics module
find all paths between two nodes python
python graph data structure
python statistics mean
python average

For given two discrete functions like this:

y1 = lambda(x): x**2
y2 = lambda(x): (x-1.5)*10
x1 = np.linspace(0,12,50)
x2 = np.linspace(1,10,12)
plot(x1, y1(x1), '.-')
plot(x2, y2(x2), '.-')

what is the shortest Python code to get the maximum of them?

Something like:

x = np.sort(np.r_[x1, x2])
y = np.maximum(np.interp(x, x1, y1(x1)), np.interp(x, x2, y2(x2)))

statistics — Mathematical statistics functions — Python 3.8.5 , This module provides functions for calculating mathematical statistics of Single mode (most common value) of discrete or nominal data. mean([1, 2, 3, 4, 4]) 2.8 >>> mean([-1.0, 2.5, 3.25, 5.75]) 2.625 >>> from If the smallest or largest of those is desired instead, use min(multimode(data)) or max(multimode(data)) . In another Python Patterns column, I will try to analyze their running speed and improve their performance, at the cost of more code. UPDATE: Eryk Kopczyński pointed out that these functions are not optimal. To the contrary, "this program runs in exponential time, while find_shortest_path can be done in linear time using BFS [Breadth First

Well you just need to use the built-in max function on the y values.

f1max = max([y1(x) for x in x1])
f2max = max([y2(x) for x in x2])

Python Patterns, on graph algorithms, which are an important part of discrete mathematics. For instance, caller-callee relationships in a computer program can be seen as a Let's write a simple function to determine a path between two nodes. take those labels into account (e.g. to find the shortest route between two cities on a map). Shortest path with exactly k edges in a directed and weighted graph; Check if given path between two nodes of a graph represents a shortest paths; Shortest path in a graph from a source S to destination D with exactly K edges for multiple Queries; Building an undirected graph and finding shortest path using Dictionaries in Python

I would do this as:

#!/usr/bin/env ipython
# -------------------
import numpy as np
import matplotlib as mpl
mpl.use('TKAgg');
from pylab import plot,show

# ---------------------
y1 = lambda(x): x**2
y2 = lambda(x): (x-1.5)*10
x1 = np.linspace(0,12,50)
x2 = np.linspace(1,10,12)
# --------------------
ymax=np.max((y1(x1),y2(x1)),axis=0)
plot(x1, y1(x1), '.-')
plot(x2, y2(x2), '.-')
plot(x1, ymax, 'k',lw=2.0)
show()

max() and min() in Python, This function is used to compute the minimum of the values passed in its argument and lexicographically smallest value if strings are passed as� Distance between two Vertices: It is the number of edges in the shortest path between two vertices. Let us try to calculate the distance between vertices A and D: Possible paths between A and D are: AB -> BC -> CD AD AB -> BD Out of these three paths, AD is the shortest having only one edge. Hence, the distance between A and D is 1.

Python Advanced: Graph Theory and Graphs in Python, This code generates the following output, if combined with the previously defined graph The following Python function calculates the isolated nodes of a given graph: Edges are represented as sets with one (a loop back to the vertex) or two vertices We want to find now the shortest path from one node to another node. A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

Python Statistics Fundamentals: How to Describe Your Data – Real , You'll find out how to describe, summarize, and represent your data 5 Python + Matplotlib examples with full source code that you can use as a The function mean() and method .mean() from NumPy return the u = [2, 3, 2, 8, 12] >>> mode_ = max((u.count(item), item) for item in set(u))[1] >>> mode_ 2. A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

How to Use an Empirical Distribution Function in Python, There are two main types of probability distribution functions we may need to For discrete data, the PDF is referred to as a Probability Mass Function (PMF). as the observations in the domain are enumerated from smallest to largest. Click to sign-up and also get a free PDF Ebook version of the course. We will first get input values from user using input() and convert it to float using float(). We will use the BMI formula, which is weight/(height**2). Then print the result using conditional statements. Here we have used elif because once we satisfy a condition we don’t want to check the rest of the statements. Program to calculate BMI in Python

Comments
  • What is the one that you've tried?
  • you mean the max for every point?
  • Yes, in each point. Note, the functions defined at different points.
  • @DirtyBit that will not work, because you have to use max on an iterable. You are using it on a function.
  • Oh, yes iterate over it for each point!
  • Thanks! Amazing python!