system.run(samples)
initalConfig, configChanges = system.run(samples)
system.toFile('main', initalConfig, configChanges)
obsList = system.fromFile("main_" + str(dimensionRoot) + '_' +
str(samples + 1) + ".npy")
X = np.zeros((dimensionRoot * dimensionRoot, samples))
for idx in range(samples):
X[:, idx] = obsList.reshape(-1, dimensionRoot * dimensionRoot)[idx]
wantedDim = 3
pX, eigVec = pca.pca(X, 3, "cov", p=0.8, returnEigVec=True)
k = len(eigVec)
spinGrids = []
for i in range(k):
spinGrids.append(eigVec[i].reshape((dimensionRoot, dimensionRoot)))
# define the grid over which the function should be plotted (xx and yy are matrices)
x = np.arange(0, dimensionRoot + 1, 1.)
xx, yy = np.meshgrid(x, x)
for i in range(k):
# fill a matrix with the function values
zz = spinGrids[i]
system.run(samples)
initalConfig, configChanges = system.run(samples)
system.toFile('main', initalConfig, configChanges)
obsList = system.fromFile("main_"+str(dimensionRoot)+'_'+str(samples+1)+".npy")
X = np.zeros((dimensionRoot * dimensionRoot, samples))
for idx in range(samples):
X[:,idx] = obsList.reshape(-1, dimensionRoot * dimensionRoot)[idx]
wantedDim = 3
pX, eigVec = pca.pca(X, 3, "cov", p = 0.8, returnEigVec=True)
k = len(eigVec)
spinGrids = []
for i in range ( k ):
spinGrids.append( eigVec[i].reshape( (dimensionRoot, dimensionRoot) ) )
# define the grid over which the function should be plotted (xx and yy are matrices)
x = np.arange(0, dimensionRoot + 1, 1.)
xx, yy = np.meshgrid(x,x)
for i in range( k ):
for idx in range(observations):
X[:,idx] = obsList.reshape(-1, sqrtFeat * sqrtFeat)[idx]
print "Reshape Array:", time.time()-readTime
# Determine starting time
pcaStartTime = time.time()
# Given a MxN numpy-array with M features and N samples, this will return
# a numpy-array with k features and N samples, where every sample has been
# projected onto the first k features of greatest/largest variance.
if args.gpu: # Utilize gpu
from pca import pcaGpu
pX = pcaGpu.PCAGpu(X, args.k, args.m).pca()
elif args.plotEigVec:
pX, eigVec = pca.pca(X, args.k, args.m, p=args.p, returnEigVec=True)
else:
pX = pca.pca(X, args.k, args.m, p=args.p)
pcaTotalTime = time.time() - pcaStartTime
print "PCA ran:", pcaTotalTime
# Have a look
if args.plot3D:
ax = plt.figure().add_subplot(111, projection="3d")
c = cm.rainbow(np.linspace(0, 1, pX.shape[1]))
ax.scatter(pX[0,:], pX[1,:], pX[2,:], ".", color=c)
plt.draw()
if args.plotEigVec:
k = len(eigVec)
