def main():
'''
Entry point of Postnovo app.
'''
start_time = time()
test_argv = None
userargs.setup(test_argv)
if config.globals['Retrain']:
classifier.train_models()
else:
input.parse()
single_alg_prediction_df = singlealg.do_single_alg_procedure()
##REMOVE: for debugging
#utils.save_pkl_objects(
# config.globals['Output Directory'],
# **{'single_alg_prediction_df.pkl': single_alg_prediction_df})
##REMOVE: for debugging
#single_alg_prediction_df = utils.load_pkl_objects(
# config.globals['Output Directory'],
# 'single_alg_prediction_df.pkl')
consensus_prediction_df = consensus.do_consensus_procedure()
##REMOVE: for debugging
#utils.save_pkl_objects(
# config.globals['Output Directory'],
# **{'consensus_prediction_df.pkl': consensus_prediction_df})
##REMOVE: for debugging
#consensus_prediction_df = utils.load_pkl_objects(
# config.globals['Output Directory'],
# 'consensus_prediction_df.pkl')
prediction_df = pd.concat([
single_alg_prediction_df.reset_index(),
consensus_prediction_df.reset_index()
],
ignore_index=True)
if 'index' in prediction_df.columns:
prediction_df.drop('index', axis=1, inplace=True)
prediction_df = masstol.do_mass_tol_procedure(prediction_df)
prediction_df = interspec.do_interspec_procedure(prediction_df)
classifier.classify(prediction_df)
##REMOVE: for debugging
#classifier.classify(None)
print('Postnovo successfully completed')
utils.verbose_print('Total time elapsed:', time() - start_time)
return
def main():
weights, values = parse(sys.argv[1])
weight_bound = int(prompt("Weight Bound"))
matrix = knapsack(weights, values, weight_bound)
score = matrix[-1][-1] ##Best score will always be in the last position
items = walkback(weights, values, matrix)
print "The best score is %d and the items are %s" % (score, str(items))
def main():
if len(sys.argv) != 2:
usage()
sys.exit(1)
edges = dedupe(parse(get_lines(sys.argv[1])))
kruskals_output, _ = kruskals(edges)
prims_output, _ = prims(edges)
print_edges(prims_output, "primout.txt")
print_edges(kruskals_output, "kruskalout.txt")
def __init__(self, filepath="b_should_be_easy.in"):
data = parse(filepath)
self.cars = []
for vehicle_id in range(data.get('vehicles')):
self.cars.append(Car(vehicle_id))
self.rides = []
for ride_id in data.get('rides'):
ride_info = data.get('rides').get(ride_id)
self.rides.append(Ride(ride_id, **ride_info))
self.rows = data.get('rows')
self.columns = data.get('columns')
self.number_of_rides = data.get('number_of_rides')
self.bonus = data.get('bonus')
self.total_steps = data.get('total_steps')
def main(filename):
with open(filename, 'r') as f:
edges = parse(get_lines(filename))
graph = make_graph(edges)
source = raw_input("Source vertex:")
while not in_graph(graph, source):
source = raw_input("Bad source\nSource vertex:")
vec, back_edges = topological_sort(graph)
negs = False
for _, _, w in edges:
if w < 0:
negs = True
cycle = len(back_edges) > 0
if not cycle:
print "Graph is a DAG, running DAG SP algorithm"
d, parent = dag_sp(graph, vec, source)
elif not negs:
print "Graph has no negative edges, running Dijkstra's algorithm"
d, parent = dijkstra(graph, source)
else:
print "Running Bellman-Ford algorithm"
d, parent = bellman_ford(graph, source)
if d == None: ## Sufficent to assume parent is also None
print "Graph contains a negative-weight cycle."
sys.exit(0)
while True:
dest = raw_input("Destination vertex:")
while not in_graph(graph, dest):
dest = raw_input("Bad destination\nDestination vertex:")
dist, order = get_shortest_path(d, parent, dest)
if dist >= INFINITY:
print "There is no way to reach the destination from the source"
else:
print "The distance between the vertices is %d" % dist
print "The shortest path is %s" % pprint_path(order)
def main():
weights, values = parse(sys.argv[1])
weight_bound = int(prompt("Weight Bound"))
items, score = knapsack(weights, values, weight_bound)
print "The best score is %d and the items are %s" % (score, str(items))
#!/usr/bin/env python from input import get_lines, parse from graph import make_graph, topological_sort import sys from os.path import isfile filename = sys.argv[1] if not isfile(filename): exit(1) lines = get_lines(filename) edges = parse(lines) graph = make_graph(edges) path, back_edges = topological_sort(graph) if not back_edges: print "The path is: " + str(path) else: print "The graph is cyclic. Back edges: " + str(back_edges)
import input
import knapsack
import sys
def pprint_2d_arr(arr):
for row in arr[::-1]:
s = ""
for elem in row:
s += "%3d," % elem
print s
tup = input.parse("knapsack.txt")
output = knapsack.knapsack(*tup, weight_bound=int(sys.argv[1]))
print output
from input import parse, get_lines, dedupe
from graph import make_weighted_graph
from sorts import merge_sort, PriorityQueue
from mst import kruskals, prims
edges = dedupe(parse(get_lines("graph.txt")))
print kruskals(edges)
print prims(edges)
from input import get_lines, parse
from graph import make_graph, topological_sort
from sp import dag_sp, bellman_ford, dijkstra, get_shortest_path
edges = parse(get_lines("dag.txt"))
graph = make_graph(edges)
vec, backs = topological_sort(graph)
negs = False
for _, _, w in edges:
if w < 0:
negs = True
cycle = len(backs) > 0
if negs:
print "Negatives"
if cycle:
print "Cyclic"
print backs
dag_sol = dag_sp(graph, vec, '1')
bf_sol = bellman_ford(graph, '1')
dijk_sol = dijkstra(graph, '1')
print dag_sol
print bf_sol
print dijk_sol
d, parent = dag_sol
print get_shortest_path(d, parent, '5')
from input import parse, print_info, check_area, add_new_one, place_corral
if __name__ == '__main__': # main function
# CN = parse(input('Enter list of animals:')) # enter animal list from keyboard
CN = parse(open('animals.txt',
'r').read()) # read animal list from file "animals.txt"
place_corral(CN) # set corrals from animal list
print(f'Corral needed: {len(set(CN))}') # how many corrals are needed
print_info() # print to console info about animal and corral
# checks whether it is possible to accommodate all newly arrived animals
check_area(
open('new_animals.txt',
'r').read()) # read animal list from file "new_animals.txt"
while 1: # the user alternately accommodates each newly arrived animal
info = input(
"Enter 'e' for exit\nEnter animal kind and corral id(lion,1):")
if info == 'e':
break
add_new_one(info.split(',')[0], int(info.split(',')[1]))
print_info() # print to console info about animal and corral
