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

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 3. Capítulo 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

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 3. Capítulo 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

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

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

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 3. Capítulo 1

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones