def __compareVectToNonVectorOneChange__(G, p_in1, p_out1, c_in1, c_out1):
A, At, C, Ct, n, s1, s2 = hm.__setup__(G)
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
As, Ats, totals = hm.__setupOneAtATime__(A, At, oldVer)
order = list(range(4))
rd.shuffle(order)
for t1 in order:
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
p_out, c_in, c_out, p_in = hmNV.__getNormNonVect__(
p_out, c_in, c_out, p_in)
## make copy here just in case of modification
oldVerOrig = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
slowRes = slowMethod(G, p_in, p_out, c_in, c_out, t1)
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
p_out, c_in, c_out, p_in = hmNV.__getNormNonVect__(
p_out, c_in, c_out, p_in)
reallySlow = hitsReallySimple(G, p_in, p_out, c_in, c_out, t1)
C2 = C + Ct
oldVer = hm.__hitsmk2GroupOrder__(A, At, C, Ct, oldVer, s1, C2, t1)
assert (abs(oldVer[:, t1] - slowRes[:, t1]).max() < 10**(-8))
assert (abs(oldVer[:, t1] - reallySlow[:, t1]).max() < 10**(-8))
def __compareVectToNonVectorOneChange__(G, p_in1, p_out1, c_in1, c_out1):
A, At, C, Ct, n, s1, s2 = hm.__setup__(G)
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
As, Ats, totals = hm.__setupOneAtATime__(A, At, oldVer)
for t1 in range(len(p_in1)):
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
p_out, c_in, c_out, p_in = hmNV.__getNormNonVect__(
p_out, c_in, c_out, p_in)
## make copy here just in case of modification
oldVerOrig = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
slowRes = slowMethod(G, p_in, p_out, c_in, c_out, t1)
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
p_out, c_in, c_out, p_in = hmNV.__getNormNonVect__(
p_out, c_in, c_out, p_in)
reallySlow = hitsReallySimple(G, p_in, p_out, c_in, c_out, t1)
x, max1 = hm.__hitsmk2OneAtATimeGroup__(As, Ats, t1, C, Ct, oldVer, s1,
s2)
assert (abs(oldVer - slowRes).max() < 10**(-8))
assert (abs(oldVer - reallySlow).max() < 10**(-8))
def __compareVectToNonVectorAllChanges__(G, p_in1, p_out1, c_in1, c_out1):
A, At, C, Ct, n, s1, s2 = hm.__setup__(G)
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
As, Ats, totals = hm.__setupOneAtATime__(A, At, oldVer)
for rep in range(10):
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
p_out, c_in, c_out, p_in = hmNV.__getNormNonVect__(
p_out, c_in, c_out, p_in)
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
order = list(range(len(p_in1)))
rd.shuffle(order)
maxDiff = -100
for t1 in order:
## make copy here just in case of modification
# oldVerOrig = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
# oldVerOrig1 = oldVer.copy()
reallySlow = hitsReallySimple(G, p_in.copy(), p_out.copy(),
c_in.copy(), c_out.copy(), t1)
p_in, p_out, c_in, c_out = slowMethod(G, p_in, p_out, c_in, c_out,
t1, False)
x, max1 = hm.__hitsmk2OneAtATimeGroup__(As, Ats, t1, C, Ct, oldVer,
s1, s2)
if max1 > maxDiff:
maxDiff = max1
slowRes = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
assert (abs(oldVer - slowRes).max() < 10**(-8))
assert (abs(oldVer - reallySlow).max() < 10**(-8))
p_in2 = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out2 = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in2 = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out2 = np.array(deepcopy(c_out1)).reshape([len(G), 1])
p_out2, c_in2, c_out2, p_in2 = hmNV.__getNormNonVect__(
p_out2, c_in2, c_out2, p_in2)
original = np.concatenate([p_out2, c_in2, c_out2, p_in2], axis=1)
actualMaxDiff = abs(original - oldVer).max()
p_outM = abs(p_out2 - p_out).max()
c_outM = abs(c_out2 - c_out).max()
p_inM = abs(p_in2 - p_in).max()
c_inM = abs(c_in2 - c_in).max()
slowMax = max([p_outM, c_outM, p_inM, c_inM])
assert (abs(actualMaxDiff - maxDiff) < 10**(-10))
assert (abs(actualMaxDiff - slowMax) < 10**(-10))
assert (abs(maxDiff - slowMax) < 10**(-10))
def __compareVectToNonVectorOneChange__(G, p_in1, p_out1, c_in1, c_out1):
func1 = hmV.__getNormNonVect__
func2 = hm.__hitsmk2OneAtATime__
for t1 in range(len(p_in1)):
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
p_out, c_in, c_out, p_in = func1(p_out, c_in, c_out, p_in)
# make copy here just in case of modification
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
slowRes = slowMethod(G, p_in, p_out, c_in, c_out, t1)
A, At, C, Ct, n, s1, s2 = hm.__setup__(G)
As, Ats, totals = hm.__setupOneAtATime__(A, At, oldVer)
func2(As, Ats, t1, C, Ct, oldVer, s1, s2, totals)
assert (abs(oldVer - slowRes).max() < 10**(-8))
assert (abs(oldVer.sum(axis=0) - totals).max() < 10**(-10))
def __compareVectToNonVectorAllChanges__(G, p_in1, p_out1, c_in1, c_out1):
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
p_out, c_in, c_out, p_in = hmV.__getNormNonVect__(p_out, c_in, c_out, p_in)
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
order = list(range(4))
rd.shuffle(order)
A, At, C, Ct, n, s1, s2 = hm.__setup__(G)
As, Ats, totals = hm.__setupOneAtATime__(A, At, oldVer)
slwM = slowMethod
func1 = hm.__hitsmk2Order__
C2 = C + Ct
for t1 in order:
# make copy here just in case of modification
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
p_in, p_out, c_in, c_out = slwM(G, p_in, p_out, c_in, c_out, t1, False)
oldVer = func1(A, At, C, Ct, oldVer, s1, C2, t1)
slowRes = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
assert (abs(oldVer[:, t1] - slowRes[:, t1]).max() < 10**(-8))
takeNorm = hmV.__getNormNonVect__
p_out, c_in, c_out, p_in = takeNorm(p_out, c_in, c_out, p_in)
## This test is a little redundant but there you go
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
p_in2 = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out2 = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in2 = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out2 = np.array(deepcopy(c_out1)).reshape([len(G), 1])
gtNormNoVec = hmV.__getNormNonVect__
p_out2, c_in2, c_out2, p_in2 = gtNormNoVec(p_out2, c_in2, c_out2, p_in2)
original = np.concatenate([p_out2, c_in2, c_out2, p_in2], axis=1)
actualMaxDiff = abs(original - oldVer).max()
p_outM = abs(p_out2 - p_out).max()
c_outM = abs(c_out2 - c_out).max()
p_inM = abs(p_in2 - p_in).max()
c_inM = abs(c_in2 - c_in).max()
slowMax = max([p_outM, c_outM, p_inM, c_inM])
assert (abs(actualMaxDiff - slowMax) < 10**(-10))
def __compareVectToNonVectorAllChanges__(G, p_in1, p_out1, c_in1, c_out1):
p_in = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out = np.array(deepcopy(c_out1)).reshape([len(G), 1])
p_out, c_in, c_out, p_in = hmV.__getNormNonVect__(p_out, c_in, c_out, p_in)
oldVer = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
order = list(range(len(p_in1)))
rd.shuffle(order)
A, At, C, Ct, n, s1, s2 = hm.__setup__(G)
As, Ats, totals = hm.__setupOneAtATime__(A, At, oldVer)
maxDiff = -100
slwM = slowMethod
func1 = hm.__hitsmk2OneAtATime__
for t1 in order:
# make copy here just in case of modification
p_in, p_out, c_in, c_out = slwM(G, p_in, p_out, c_in, c_out, t1, False)
max1 = func1(As, Ats, t1, C, Ct, oldVer, s1, s2, totals)
if max1 > maxDiff:
maxDiff = max1
slowRes = np.concatenate([p_out, c_in, c_out, p_in], axis=1)
assert (abs(oldVer - slowRes).max() < 10**(-8))
assert (abs(oldVer.sum(axis=0) - totals).max() < 10**(-10))
p_in2 = np.array(deepcopy(p_in1)).reshape([len(G), 1])
p_out2 = np.array(deepcopy(p_out1)).reshape([len(G), 1])
c_in2 = np.array(deepcopy(c_in1)).reshape([len(G), 1])
c_out2 = np.array(deepcopy(c_out1)).reshape([len(G), 1])
gtNormNoVec = hmV.__getNormNonVect__
p_out2, c_in2, c_out2, p_in2 = gtNormNoVec(p_out2, c_in2, c_out2, p_in2)
original = np.concatenate([p_out2, c_in2, c_out2, p_in2], axis=1)
actualMaxDiff = abs(original - oldVer).max()
p_outM = abs(p_out2 - p_out).max()
c_outM = abs(c_out2 - c_out).max()
p_inM = abs(p_in2 - p_in).max()
c_inM = abs(c_in2 - c_in).max()
slowMax = max([p_outM, c_outM, p_inM, c_inM])
assert (abs(actualMaxDiff - maxDiff) < 10**(-10))
assert (abs(actualMaxDiff - slowMax) < 10**(-10))
assert (abs(maxDiff - slowMax) < 10**(-10))