def problem81(matrix,graph,n_rows, n_cols):
for r in xrange(n_rows):
for c in xrange(n_cols):
cur = matrix[r][c]
# right
if(c < n_cols - 1):
right = matrix[r][c+1]
graph.add_edge((cur, right), wt=right.value)
# bottom, don't allow leftmost column to go down
if(r < n_rows - 1 and c > 0):
bottom = matrix[r+1][c]
graph.add_edge((cur, bottom), wt=bottom.value)
_, node_to_cost = pygraph.algorithms.minmax.shortest_path(graph, matrix[0][0])
goal = matrix[n_rows - 1][n_cols - 1]
print goal, node_to_cost[goal]
def problem81(matrix, graph, n_rows, n_cols):
for r in xrange(n_rows):
for c in xrange(n_cols):
cur = matrix[r][c]
# right
if (c < n_cols - 1):
right = matrix[r][c + 1]
graph.add_edge((cur, right), wt=right.value)
# bottom, don't allow leftmost column to go down
if (r < n_rows - 1 and c > 0):
bottom = matrix[r + 1][c]
graph.add_edge((cur, bottom), wt=bottom.value)
_, node_to_cost = pygraph.algorithms.minmax.shortest_path(
graph, matrix[0][0])
goal = matrix[n_rows - 1][n_cols - 1]
print goal, node_to_cost[goal]
def build_knn_graph(raw_data):
"""
:param raw_data: (pts_num, 3)
:return:
"""
nbrs = spatial.cKDTree(raw_data)
dists, idxs = nbrs.query(raw_data, k=5)
## build graph
graph = pygraph.classes.graph.graph()
graph.add_nodes(xrange(len(raw_data)))
sid = 0
for idx, dist in zip(idxs, dists):
for eid, d in zip(idx, dist):
if not graph.has_edge((sid, eid)) and eid < len(raw_data):
graph.add_edge((sid, eid), d)
sid = sid + 1
return graph
def problem82(matrix, graph,n_rows, n_cols):
for r in xrange(n_rows):
for c in xrange(n_cols):
cur = matrix[r][c]
# right
if(c < n_cols - 1):
right = matrix[r][c+1]
graph.add_edge((cur, right), wt=right.value)
# bottom, don't allow leftmost column to go down
if(r < n_rows - 1 and c > 0):
bottom = matrix[r+1][c]
graph.add_edge((cur, bottom), wt=bottom.value)
# top, don't allow leftmost column to go down
if(r > 0 and c > 0):
top = matrix[r-1][c]
graph.add_edge((cur, top), wt=top.value)
min_cost = 10e100
for r in xrange(n_rows):
print "row", r
path, distances = pygraph.algorithms.minmax.shortest_path(graph, matrix[r][0])
for r2 in xrange(n_rows):
cur = matrix[r2][n_cols - 1]
if cur in distances and distances[cur] < min_cost:
min_cost = distances[cur]
min_start = matrix[r][1]
min_end = cur
min_path = path
print min_cost, min_start, min_end
def problem82(matrix, graph, n_rows, n_cols):
for r in xrange(n_rows):
for c in xrange(n_cols):
cur = matrix[r][c]
# right
if (c < n_cols - 1):
right = matrix[r][c + 1]
graph.add_edge((cur, right), wt=right.value)
# bottom, don't allow leftmost column to go down
if (r < n_rows - 1 and c > 0):
bottom = matrix[r + 1][c]
graph.add_edge((cur, bottom), wt=bottom.value)
# top, don't allow leftmost column to go down
if (r > 0 and c > 0):
top = matrix[r - 1][c]
graph.add_edge((cur, top), wt=top.value)
min_cost = 10e100
for r in xrange(n_rows):
print "row", r
path, distances = pygraph.algorithms.minmax.shortest_path(
graph, matrix[r][0])
for r2 in xrange(n_rows):
cur = matrix[r2][n_cols - 1]
if cur in distances and distances[cur] < min_cost:
min_cost = distances[cur]
min_start = matrix[r][1]
min_end = cur
min_path = path
print min_cost, min_start, min_end
