def estNum(neigh,epsilon): m=len(neigh); if epsilon<0: return len([i for i in neigh if i>0]); sc=[0.0 for i in range(0,m+1)]; for i in range(0,m): if neigh[i]>0: sc[i]=-epsilon*neigh[i]/2.0; else: sc[i]=epsilon*(neigh[i]-1)/2.0; for i in range(0,m+1): sc[i]=math.exp(sc[i]); sm=sum(sc); sc=[i/sm for i in sc] multi=mult(1,sc); ret=min([i for i in range(0,m+1) if multi[i]>0]); if ret==m: return len([i for i in neigh if i>0]); val=neigh[ret]; v1=len([i for i in neigh if i>val]); v2=len([i for i in neigh if i==val]); return rand.randint(v1,v1+v2-1);
def estNum(MU,y,pval,epsilon):
#bnd_sc=math.sqrt(chi2.ppf((1.0-pval),df=1));
bnd_sc=math.sqrt(pval);
ret=MU.normY(y);
nm=ret[0];
sen=ret[1];
n=len(y);
if sen>0:
bnd_est=bnd_sc*abs(nm+Lap(0.0,2.0*sen/epsilon));
if MU.k>0:
bnd_est=bnd_est/math.sqrt(n-MU.k-1);
else:
bnd_est=bnd_sc*nm;
neigh=MU.neighDist(y,bnd_est);
m=len(neigh);
if epsilon<0:
return len([i for i in neigh if i>0]);
sc=[0.0 for i in range(0,m+1)];
for i in range(0,m):
if neigh[i]>0:
sc[i]=-epsilon*neigh[i]/4.0;
else:
sc[i]=epsilon*(neigh[i]-1)/4.0;
for i in range(0,m+1):
sc[i]=math.exp(sc[i]);
sm=sum(sc);
sc=[i/sm for i in sc]
multi=mult(1,sc);
ret=min([i for i in range(0,m+1) if multi[i]>0]);
if ret==m:
return len([i for i in neigh if i>0]);
val=neigh[ret];
v1=len([i for i in neigh if i>val]);
v2=len([i for i in neigh if i==val]);
return rand.randint(v1,v1+v2-1);
def expPick(sc,mret):
m=len(sc);
fnd=[0 for i in range(0,mret)]
scores=[s for s in sc]
for i in range(0,mret):
ms=max(scores);
scores=[s-ms for s in scores];
prb=[math.exp(s) for s in scores];
sm=sum(prb);
prb=[s/sm for s in prb];
while sum(prb)>1:
sm=sum(prb);
prb=[s/sm for s in prb];
mlt=mult(1,prb);
k=min([k for k in range(0,m) if mlt[k]>0]);
fnd[i]=k;
scores[k]=-float("inf");
def sample(self, N=100, shuffle=True):
"""
Generate N samples from the model. Returns three numpy arrays: z2 of size (N,) with cluster assignments,
x = (N,d) with the observations, and z with the cluster counts (used for debugging only).
"""
z = mult(N, self.phi,
size=1).reshape(self.K) # these are the cluster assignments
x = np.zeros((N, self.d))
z2 = np.zeros(
N, dtype=int) # To rexpress z as vector with cluster assignments
ct = 0
for k in np.arange(self.K):
x[ct:ct + z[k], :] = mnorm(self.mu[k], self.Sigma[k], z[k])
z2[ct:ct + z[k]] = k
ct += z[k]
if shuffle:
idx = np.random.permutation(np.arange(N))
z2 = z2[idx]
x = x[idx, :]
return (z2, x, z)
def expPick(sc,mret):
m=len(sc);
fnd=[0 for i in range(0,mret)]
scores=[s for s in sc]
for i in range(0,mret):
ms=max(scores);
scores=[s-ms for s in scores];
prb=[math.exp(s) for s in scores];
sm=sum(prb);
prb=[s/sm for s in prb];
while sum(prb)>1:
sm=sum(prb);
prb=[s/sm for s in prb];
mlt=mult(1,prb);
k=min([k for k in range(0,m) if mlt[k]>0]);
fnd[i]=k;
scores[k]=-float("inf");
return fnd;
def expPick(sc, mret):
m = len(sc)
fnd = [0 for i in range(0, mret)]
scores = [s for s in sc]
for i in range(0, mret):
ms = max(scores)
scores = [s - ms for s in scores]
prb = [math.exp(s) for s in scores]
sm = sum(prb)
prb = [s / sm for s in prb]
while sum(prb) > 1:
sm = sum(prb)
prb = [s / sm for s in prb]
mlt = mult(1, prb)
k = min([k for k in range(0, m) if mlt[k] > 0])
fnd[i] = k
scores[k] = -float("inf")
return fnd
def estNum(MU, y, pval, epsilon):
#bnd_sc=math.sqrt(chi2.ppf((1.0-pval),df=1));
bnd_sc = math.sqrt(pval)
ret = MU.normY(y)
nm = ret[0]
sen = ret[1]
n = len(y)
if sen > 0:
bnd_est = bnd_sc * abs(nm + Lap(0.0, 2.0 * sen / epsilon))
if MU.k > 0:
bnd_est = bnd_est / math.sqrt(n - MU.k - 1)
else:
bnd_est = bnd_sc * nm
neigh = MU.neighDist(y, bnd_est)
m = len(neigh)
if epsilon < 0:
return len([i for i in neigh if i > 0])
sc = [0.0 for i in range(0, m + 1)]
for i in range(0, m):
if neigh[i] > 0:
sc[i] = -epsilon * neigh[i] / 4.0
else:
sc[i] = epsilon * (neigh[i] - 1) / 4.0
for i in range(0, m + 1):
sc[i] = math.exp(sc[i])
sm = sum(sc)
sc = [i / sm for i in sc]
multi = mult(1, sc)
ret = min([i for i in range(0, m + 1) if multi[i] > 0])
if ret == m:
return len([i for i in neigh if i > 0])
val = neigh[ret]
v1 = len([i for i in neigh if i > val])
v2 = len([i for i in neigh if i == val])
return rand.randint(v1, v1 + v2 - 1)
def estNum(MU,y,pval,epsilon):
bnd_sc=math.sqrt(chi2.ppf((1.0-pval),df=1));
[nm,sen]=MU.normY(self,y);
if sen>0:
bnd_est=bnd_sc*abs(nm+Lap(0.0,2.0*sen/epsilon));
else:
bnd_est=bnd_sc*nm;
neigh=MU.neighDist(y,bnd_est);
m=len(neigh);
if epsilon<0:
return len([i for i in neigh if i>0]);
sc=[0.0 for i in range(0,m+1)];
for i in range(0,m):
if neigh[i]>0:
sc[i]=-.5*epsilon*neigh[i]/2.0;
else:
sc[i]=.5*epsilon*(neigh[i]-1)/2.0;
for i in range(0,m+1):
sc[i]=math.exp(sc[i]);
sm=sum(sc);
sc=[i/sm for i in sc]
multi=mult(1,sc);
ret=min([i for i in range(0,m+1) if multi[i]>0]);
if ret==m:
return len([i for i in neigh if i>0]);
val=neigh[ret];
v1=len([i for i in neigh if i>val]);
v2=len([i for i in neigh if i==val]);
return rand.randint(v1,v1+v2-1);
