def experiment(self, name_text_file):
inputs = DataClass()
#name_text_file = input()
inputs.Read(str(name_text_file))
# Initialize graph
g = Graph()
# Initialize nodes on the graph
g.set_node_names(inputs.names)
# This can be used to reduce computation to reduce the number of connected nodes that are defined, however it is preferred not to,
# because the weight in the graph might be chosen to be defined differently
# visited=[]
for i in range(0, inputs.n + 2):
for j in range(0, inputs.n + 2):
# visited.append(i)
if j != i:
calculate_risk = np.linalg.norm(
inputs.input_information[str(i)] -
inputs.input_information[str(j)])
# insert edge betweem two nodes along with the corresponding weight
g.insert_edge(calculate_risk, int(i), int(j))
else:
continue
return g.dijkstar_output(0)
import numpy as np
from Find_Max_Path import Graph
from dataclass import DataClass
#Read data
inputs = DataClass()
inputs.Read('data')
#Initialize graph
g = Graph()
#Initialize nodes on the graph
g.set_node_names(inputs.names)
#This can be used to reduce computation to reduce the number of connected nodes that are defined, however it is preferred not to,
#because the weight in the graph might be chosen to be defined differently
#visited=[]
for i in range(0, inputs.n + 2):
for j in range(0, inputs.n + 2):
#visited.append(i)
if j != i:
calculate_risk = np.linalg.norm(inputs.input_information[str(i)] -
inputs.input_information[str(j)])
#insert edge betweem two nodes along with the corresponding weight
g.insert_edge(calculate_risk, int(i), int(j))
else:
continue
import pprint
pp = pprint.PrettyPrinter(indent=2)