def __init__(self,
y,
w,
transform="R",
permutations=PERMUTATIONS,
star=False):
y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
self.w = w
self.w_original = w.transform
self.w.transform = self.w_transform = transform.lower()
self.permutations = permutations
self.star = star
self.calc()
self.p_norm = np.array(
[1 - stats.norm.cdf(np.abs(i)) for i in self.Zs])
if permutations:
self.__crand()
sim = np.transpose(self.rGs)
above = sim >= self.Gs
larger = sum(above)
low_extreme = (self.permutations - larger) < larger
larger[low_extreme] = self.permutations - larger[low_extreme]
self.p_sim = (larger + 1.0) / (permutations + 1)
self.sim = sim
self.EG_sim = sim.mean(axis=0)
self.seG_sim = sim.std(axis=0)
self.VG_sim = self.seG_sim * self.seG_sim
self.z_sim = (self.Gs - self.EG_sim) / self.seG_sim
self.p_z_sim = 1 - stats.norm.cdf(np.abs(self.z_sim))
def __init__(self, y, w, permutations=PERMUTATIONS):
y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
w.transform = "B"
self.w = w
self.permutations = permutations
self.__moments()
self.y2 = y * y
y = y.reshape(
len(y),
1) # Ensure that y is an n by 1 vector, otherwise y*y.T == y*y
self.den_sum = (y * y.T).sum() - (y * y).sum()
self.G = self.__calc(self.y)
self.z_norm = (self.G - self.EG) / np.sqrt(self.VG)
self.p_norm = 1.0 - stats.norm.cdf(np.abs(self.z_norm))
if permutations:
sim = [
self.__calc(np.random.permutation(self.y))
for i in range(permutations)
]
self.sim = sim = np.array(sim)
above = sim >= self.G
larger = sum(above)
if (self.permutations - larger) < larger:
larger = self.permutations - larger
self.p_sim = (larger + 1.0) / (permutations + 1.0)
self.EG_sim = sum(sim) / permutations
self.seG_sim = sim.std()
self.VG_sim = self.seG_sim**2
self.z_sim = (self.G - self.EG_sim) / self.seG_sim
self.p_z_sim = 1.0 - stats.norm.cdf(np.abs(self.z_sim))
def __init__(self, y, w, permutations=PERMUTATIONS):
y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
w.transform = "B"
self.w = w
self.permutations = permutations
self.__moments()
self.y2 = y * y
y = y.reshape(len(y), 1) # Ensure that y is an n by 1 vector, otherwise y*y.T == y*y
self.den_sum = (y * y.T).sum() - (y * y).sum()
self.G = self.__calc(self.y)
self.z_norm = (self.G - self.EG) / np.sqrt(self.VG)
self.p_norm = 1.0 - stats.norm.cdf(np.abs(self.z_norm))
if permutations:
sim = [self.__calc(np.random.permutation(self.y))
for i in range(permutations)]
self.sim = sim = np.array(sim)
above = sim >= self.G
larger = sum(above)
if (self.permutations - larger) < larger:
larger = self.permutations - larger
self.p_sim = (larger + 1.0) / (permutations + 1.)
self.EG_sim = sum(sim) / permutations
self.seG_sim = sim.std()
self.VG_sim = self.seG_sim ** 2
self.z_sim = (self.G - self.EG_sim) / self.seG_sim
self.p_z_sim = 1. - stats.norm.cdf(np.abs(self.z_sim))
def __init__(self, y, w, transform='R', permutations=PERMUTATIONS, star=False):
y = np.asarray(y).flatten()
self.n = len(y)
self.y = y
self.w = w
self.w_original = w.transform
self.w.transform = self.w_transform = transform.lower()
self.permutations = permutations
self.star = star
self.calc()
self.p_norm = np.array(
[1 - stats.norm.cdf(np.abs(i)) for i in self.Zs])
if permutations:
self.__crand()
sim = np.transpose(self.rGs)
above = sim >= self.Gs
larger = sum(above)
low_extreme = (self.permutations - larger) < larger
larger[low_extreme] = self.permutations - larger[low_extreme]
self.p_sim = (larger + 1.0) / (permutations + 1)
self.sim = sim
self.EG_sim = sim.mean()
self.seG_sim = sim.std()
self.VG_sim = self.seG_sim * self.seG_sim
self.z_sim = (self.Gs - self.EG_sim) / self.seG_sim
self.p_z_sim = 1 - stats.norm.cdf(np.abs(self.z_sim))
def __crand(self):
y = self.y
rGs = np.zeros((self.n, self.permutations))
n_1 = self.n - 1
rid = list(range(n_1))
prange = list(range(self.permutations))
k = self.w.max_neighbors + 1
rids = np.array([np.random.permutation(rid)[0:k] for i in prange])
ids = np.arange(self.w.n)
ido = self.w.id_order
wc = self.__getCardinalities()
if self.w_transform == "r":
den = np.array(wc) + self.star
else:
den = np.ones(self.w.n)
for i in range(self.w.n):
idsi = ids[ids != i]
np.random.shuffle(idsi)
yi_star = y[i] * self.star
wci = wc[i]
rGs[i] = (y[idsi[rids[:, 0:wci]]]).sum(1) + yi_star
rGs[i] = (np.array(rGs[i]) / den[i]) / (self.y_sum -
(1 - self.star) * y[i])
self.rGs = rGs
def __crand(self):
y = self.y
rGs = np.zeros((self.n, self.permutations))
n_1 = self.n - 1
rid = list(range(n_1))
prange = list(range(self.permutations))
k = self.w.max_neighbors + 1
rids = np.array([np.random.permutation(rid)[0:k] for i in prange])
ids = np.arange(self.w.n)
ido = self.w.id_order
wc = self.__getCardinalities()
if self.w_transform == 'r':
den = np.array(wc) + self.star
else:
den = np.ones(self.w.n)
for i in range(self.w.n):
idsi = ids[ids != i]
np.random.shuffle(idsi)
yi_star = y[i] * self.star
wci = wc[i]
rGs[i] = (y[idsi[rids[:, 0:wci]]]).sum(1) + yi_star
rGs[i] = (np.array(rGs[i]) / den[i]) / (
self.y_sum - (1 - self.star) * y[i])
self.rGs = rGs
def __getCardinalities(self):
ido = self.w.id_order
self.wc = np.array(
[self.w.cardinalities[ido[i]] for i in range(self.n)])
return self.wc
def __getCardinalities(self):
ido = self.w.id_order
self.wc = np.array(
[self.w.cardinalities[ido[i]] for i in range(self.n)])
return self.wc