Preparation

This chapter is dedicated to different approaches to find minimum-weighted paths on graphs. We work with oriented weighted graphs here.

To solve problems, we’ll use a pre-implemented class Graph defined with an adjacency matrix, since each edge has some weight that will be stored in the matrix.


              1234567891011121314151617
            
class Graph:
  def __init__(self, vertices=0):
    # init graph with this number of vertices
    self.g = [[0 for _ in range(vertices)] for _ in range(vertices)]

  def addEdge(self, u, v, w, o = False):
    # u - start vertex, v - end vertex, w - weight of edge, o - is it oriented
    self.g[u][v] = w
    if not o:
      self.g[v][u] = w

  def __str__(self):
    out = ""
    for row in self.g:
      out += str(row) + ' '
    return out

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 1

Preparation

This chapter is dedicated to different approaches to find minimum-weighted paths on graphs. We work with oriented weighted graphs here.

To solve problems, we’ll use a pre-implemented class Graph defined with an adjacency matrix, since each edge has some weight that will be stored in the matrix.


              1234567891011121314151617
            
class Graph:
  def __init__(self, vertices=0):
    # init graph with this number of vertices
    self.g = [[0 for _ in range(vertices)] for _ in range(vertices)]

  def addEdge(self, u, v, w, o = False):
    # u - start vertex, v - end vertex, w - weight of edge, o - is it oriented
    self.g[u][v] = w
    if not o:
      self.g[v][u] = w

  def __str__(self):
    out = ""
    for row in self.g:
      out += str(row) + ' '
    return out

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 3. Chapter 1

Preparation

This chapter is dedicated to different approaches to find minimum-weighted paths on graphs. We work with oriented weighted graphs here.

To solve problems, we’ll use a pre-implemented class Graph defined with an adjacency matrix, since each edge has some weight that will be stored in the matrix.


              1234567891011121314151617
            
class Graph:
  def __init__(self, vertices=0):
    # init graph with this number of vertices
    self.g = [[0 for _ in range(vertices)] for _ in range(vertices)]

  def addEdge(self, u, v, w, o = False):
    # u - start vertex, v - end vertex, w - weight of edge, o - is it oriented
    self.g[u][v] = w
    if not o:
      self.g[v][u] = w

  def __str__(self):
    out = ""
    for row in self.g:
      out += str(row) + ' '
    return out

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

This chapter is dedicated to different approaches to find minimum-weighted paths on graphs. We work with oriented weighted graphs here.

To solve problems, we’ll use a pre-implemented class Graph defined with an adjacency matrix, since each edge has some weight that will be stored in the matrix.


              1234567891011121314151617
            
class Graph:
  def __init__(self, vertices=0):
    # init graph with this number of vertices
    self.g = [[0 for _ in range(vertices)] for _ in range(vertices)]

  def addEdge(self, u, v, w, o = False):
    # u - start vertex, v - end vertex, w - weight of edge, o - is it oriented
    self.g[u][v] = w
    if not o:
      self.g[v][u] = w

  def __str__(self):
    out = ""
    for row in self.g:
      out += str(row) + ' '
    return out

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 1

Switch to desktop for real-world practiceContinue from where you are using one of the options below