def test_makelist(self): ''' Test make array 1 dimention. ''' res1 = ut.makelist(10) self.assertEqual(len(res1), 10) res2 = ut.makelist(100,0) self.assertEqual(len(res2), 100)
def testprint(): ''' Test print for checking cgi and loading module. ''' testdata = ut.makelist(10) return str(testdata)
def dijkstra(route, start, end):
'''
Dijkstra route search function with priority queue.
Return tuple of most smallest route list and cost.
Argument start is start vertex index.
Argument end is end vertex index.
'''
#TODO:This is not work normality.Must be repaired.
que = qu.PriorityQueue()
vtxlen = route.vertexlen
distance = ut.makelist(length=vtxlen, initvalue=float("inf"))
distance[start] = 0
que.put((0, start)) #shorest distance, vertex number
smallroute = [start]
totalcost = 0
while que.empty() == False:
smldist = que.get()
vertexid = smldist[1]
if distance[vertexid] < smldist[0]:
continue
for vtxidx in route.edges:
edges = route.edges[vtxidx]
for edgidx in range(len(edges)):
edge = edges[edgidx]
cost = distance[vertexid]+edge.cost
if distance[edge.to] > cost:
distance[edge.to] = cost
que.put((distance[edge.to], edge.to))
smallroute.append(edge.to)
totalcost += edge.cost
return (smallroute, totalcost)
def __init__(self, size=0, values=[]): ''' Init heap with heap size and values. ''' if len(values) > 0: self.values = values else: self.values = ut.makelist(size) self.pos = 0
def fib(number): ''' fibonach function ''' memo = ut.makelist(number+1,0) def rec_fib(n): if n <= 1: return n if memo[n] != 0 : return memo[n] memo[n] = rec_fib(n-1)+rec_fib(n-2) return memo[n] return rec_fib(number)
def without_priority_queue_dijkstra(route, start, end):
'''
Dijkstra route search function
without priority queue.
'''
#TODO:This is not work normality.Must be repaired.
vtxlen = route.vertexlen
d = ut.makelist(length=vtxlen, initvalue=float("inf"))
used = ut.makelist(length=vtxlen, initvalue=False)
d[start] = 0
prev = ut.makelist(length=vtxlen, initvalue=-1)
while True:
v = -1
for u in range(vtxlen):
if (used[u] == False) and (v == -1 or d[u] < d[v]):
v = u
if v == -1:
break
used[v] = True
for u in range(vtxlen):
cost = d[v]+route.edges[v][u].cost
if d[u] > cost:
d[u] = cost
prev[u] = v
path = []
t = end
while t != -1:
t = prev[t]
path.append(t)
return (path[::-1], d[end])
def multireg(x1s, x2s, ys): ''' Multiple regression analysis. ''' ones = ut.makelist(len(x1s), 1) vecs1 = [lr.Vector([x1,x2,one]) for x1,x2,one in zip(x1s,x2s,ones)] mat1 = lr.Matrix(vecs1) vx1 = lr.Vector(x1s) vx2 = lr.Vector(x2s) vone = lr.Vector(ones) vecs2 = [vx1,vx2,vone] mat2 = lr.Matrix(vecs2) vy = lr.Vector(ys) mat3 = lr.Matrix([vy]) res = (mat1*mat2)**-1*mat1*mat3 return res.rows[0].cols
def prime(n): ''' Caluculate prime number length under "n" by Eratosthenes sieve. n: under n prime number detect and return. ''' prime = [] is_prime = ut.makelist(n+1, initvalue=True) is_prime[0] = False is_prime[1] = False i = 2 #Most minimum prime number. while i <= n: if is_prime[i] == True: prime.append(i) j = 2*i while j <= n: is_prime[j] = False j += i i += 1 return prime
def __eigen_2dim(self):
'''
Calculate eigen value for 2 dimension matrix.
'''
formula = ut.makelist(length=3, initvalue=0)
formula[0] = 1 #x^2 coef
rows = self.rows
cols1 = rows[0].cols
cols2 = rows[1].cols
a = cols1[0]
d = cols2[1]
formula[1] = a*-1+(-1)*d
formula[2] = self.det()
egs = nt.quadeq(formula)
if len(egs) <= 1:
raise ValueError("Multiple root cannot deal.")
res = {}
for eg in egs:
resA = a-eg
resC = cols2[0]
#TODO: reduct function is fault.
# if resA != 1 and resC != 1:
# if resA>resC:
# resA,resC = nt.reduct(resA, resC)
# elif resC<resA:
# resC,resA = nt.reduct(resC, resA)
# else:
# resA = resC = 1
res[eg] = Vector([-resC,resA])
return res
def __init__(self, forknum=5): self.forks = ut.makelist(length=forknum, initvalue=False)
