def MakeData(self, K=3, Nperclass=1000):
''' Creates simple toy dataset for testing.
Simple 3 component data with eye covar and distinct, well-sep means
mu0 = [-10, -10]
mu1 = [0, 0]
mu2 = [10, 10]
'''
PRNG = np.random.RandomState(8675309)
# Means: [-10 -10; 0 0; 10 10]
Mu = np.zeros((3,2))
Mu[0] = Mu[0] - 10
Mu[2] = Mu[2] + 10
# Covariances: identity
Sigma = np.eye(2)
# Generate data from K components, each with Nperclass examples
self.TrueResp = np.zeros((K*Nperclass,K))
Xlist = list()
for k in range(K):
Xcur = mvnrand(Mu[k], Sigma, Nperclass, PRNG)
Xlist.append(Xcur)
self.TrueResp[k*Nperclass:(k+1)*Nperclass, k] = 1.0
X = np.vstack(Xlist)
self.Data = XData(X=X)
self.Mu = Mu
assert np.abs(self.TrueResp.sum() - self.Data.nObs) < 1e-2
def MakeData(self, K=3, Nperclass=1000):
''' Create simple toy XData with K components, add as attribute to self
Simple 3 component data with eye covar and distinct, well-sep means
mu0 = [-10, -10]
mu1 = [0, 0]
mu2 = [10, 10]
'''
Mu = np.zeros((3, 2))
Mu[0] = Mu[0] - 10
Mu[2] = Mu[2] + 10
Sigma = np.eye(2)
self.TrueResp = np.zeros((K * Nperclass, K))
self.DupResp = np.zeros((K * Nperclass, 2 * K))
Xlist = list()
for k in range(K):
Xcur = mvnrand(Mu[k], Sigma, Nperclass)
Xlist.append(Xcur)
self.TrueResp[k * Nperclass:(k + 1) * Nperclass, k] = 1.0
start = k * Nperclass
stop = (k + 1) * Nperclass
half = 0.5 * (start + stop)
self.DupResp[start:half, k] = 1.0
self.DupResp[half:stop, K + k] = 1.0
X = np.vstack(Xlist)
self.Data = XData(X=X)
self.Mu = Mu
assert np.abs(self.TrueResp.sum() - self.Data.nObs) < 1e-2
assert np.abs(self.DupResp.sum() - self.Data.nObs) < 1e-2
def MakeData(self, K=3, Nperclass=1000):
''' Create simple toy XData with K components, add as attribute to self
Simple 3 component data with eye covar and distinct, well-sep means
mu0 = [-10, -10]
mu1 = [0, 0]
mu2 = [10, 10]
'''
Mu = np.zeros((3,2))
Mu[0] = Mu[0] - 10
Mu[2] = Mu[2] + 10
Sigma = np.eye(2)
self.TrueResp = np.zeros((K*Nperclass,K))
self.DupResp = np.zeros((K*Nperclass, 2*K))
Xlist = list()
for k in range(K):
Xcur = mvnrand( Mu[k], Sigma, Nperclass)
Xlist.append(Xcur)
self.TrueResp[k*Nperclass:(k+1)*Nperclass, k] = 1.0
start = k*Nperclass
stop = (k+1)*Nperclass
half = 0.5*(start + stop)
self.DupResp[start:half, k] = 1.0
self.DupResp[half:stop, K+k] = 1.0
X = np.vstack(Xlist)
self.Data = XData(X=X)
self.Mu = Mu
assert np.abs(self.TrueResp.sum() - self.Data.nObs) < 1e-2
assert np.abs(self.DupResp.sum() - self.Data.nObs) < 1e-2
def MakeData(self, K=3, Nperclass=1000):
''' Creates simple toy dataset for testing.
Simple 3 component data with eye covar and distinct, well-sep means
mu0 = [-10, -10]
mu1 = [0, 0]
mu2 = [10, 10]
'''
PRNG = np.random.RandomState(8675309)
# Means: [-10 -10; 0 0; 10 10]
Mu = np.zeros((3, 2))
Mu[0] = Mu[0] - 10
Mu[2] = Mu[2] + 10
# Covariances: identity
Sigma = np.eye(2)
# Generate data from K components, each with Nperclass examples
self.TrueResp = np.zeros((K * Nperclass, K))
Xlist = list()
for k in range(K):
Xcur = mvnrand(Mu[k], Sigma, Nperclass, PRNG)
Xlist.append(Xcur)
self.TrueResp[k * Nperclass:(k + 1) * Nperclass, k] = 1.0
X = np.vstack(Xlist)
self.Data = XData(X=X)
self.Mu = Mu
assert np.abs(self.TrueResp.sum() - self.Data.nObs) < 1e-2
def MakeData(self, nObsC=200):
if self.obsM is None:
return
XList = list()
np.random.seed(505)
for k in range(self.obsM.K):
Sigma = self.obsM.get_covar_mat_for_comp(k)
mu = self.obsM.get_mean_for_comp(k)
Xcur = mvnrand(mu, Sigma, nObsC)
XList.append(Xcur)
X = np.vstack(XList)
self.nObsC = nObsC
self.Data = XData(X=X)
def MakeData(self, nObsC=200):
if self.obsM is None:
return
XList = list()
np.random.seed(505)
for k in range(self.obsM.K):
Sigma = self.obsM.get_covar_mat_for_comp(k)
mu = self.obsM.get_mean_for_comp(k)
Xcur = mvnrand(mu, Sigma, nObsC)
XList.append(Xcur)
X = np.vstack(XList)
self.nObsC = nObsC
self.Data = XData(X=X)