def getCxtSubspace(wl, dim, var_threshold=0.45):
emb = []
for word in wl:
if (word not in vecDict):
print "non-exist:", word
continue
wordEmbed = dict[word]
emb.append(wordEmbed)
emb = np.array(emb)
pca = PCA()
pca.fit(emb)
varList = pca.explained_variance_ratio_
cand = 0
varSum = 0
for var in varList:
varSum += var
cand += 1
if (varSum >= var_threshold):
break
pca = PCA(n_components=cand)
pca.fit(emb)
top_embed = pca.components_
print "dim:", len(top_embed.tolist()), cand
return top_embed.tolist()
def hack_pca(filename):
'''
Input: filename -- input image file name/path
Output: img -- image without rotation
'''
# YOUR CODE HERE
# begin answer
img_r = (plt.imread(filename)).astype(np.float64)
data = []
for i in range(img_r.shape[0]):
for j in range(img_r.shape[1]):
if (img_r[i][j][3] > 0):
tt = (img_r[i][j][0] * 299 + img_r[i][j][1] * 587 +
img_r[i][j][2] * 114 + 500) / 1000
if (tt > 150):
data.append([i, j])
data = np.array(data)
w, _ = PCA(data)
result = np.dot(data, w)
result_min = np.min(result, axis=0)
result_max = np.max(result, axis=0)
result -= result_min
result_max = np.max(result, axis=0)
result[:, 0] = (result[:, 0] / result_max[0]) * 99
result[:, 1] = (result[:, 1] / result_max[1]) * 99
result_max = np.max(result, axis=0)
new_img_r = np.zeros((100, 100, 3))
for i in range(result.shape[0]):
new_img_r[-int(result[i][1])][-int(result[i][0])] = [100, 100, 100]
return new_img_r
def main():
# Load dataset
data = datasets.load_iris()
X = normalize(data.data[data.target != 0])
y = data.target[data.target != 0]
y[y == 1] = 0
y[y == 2] = 1
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
seed=1)
clf = LogisticRegression(gradient_descent=True)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Reduce dimension to two using PCA and plot the results
pca = PCA()
pca.plot_in_2d(X_test,
y_pred,
title="Logistic Regression",
accuracy=accuracy)
def getCentVec(self, contextVecs):
sample, rank, dim = contextVecs.shape
contexts = np.reshape(contextVecs, (sample * rank, dim))
pca = PCA(n_components=1)
pca.fit(contexts)
return pca.components_[0]
def pic_handle(img_data, sd, ori_size):
"""
做PCA处理之后，在还原到原来的维度，然后显示，之后输出信噪比
"""
Pca = PCA(sd, img_data)
c_data, w_star = Pca.pca() # 进行pca降维,获取投影矩阵
w_star = np.real(w_star)
print(w_star)
new_data = w_star * w_star.T * c_data + Pca.mean # 还原到原来的维度
total_img = []
# 图片混合
for i in range(Pca.data_size):
if len(total_img) == 0:
total_img = new_data[:, i].T.reshape(ori_size)
else:
total_img = np.hstack(
[total_img, new_data[:, i].T.reshape(ori_size)])
# 计算信噪比
print('信噪比:')
for i in range(Pca.data_size):
a = psnr(np.array(data[:, i].T), np.array(new_data[:, i].T))
print('图', i, '的信噪比为:', a, 'dB')
# 处理图片
total_img = np.array(total_img).astype(np.uint8)
cv2.imwrite('pca image.jpg', total_img) # 图片显示
cv2.imshow('pca image', total_img)
cv2.waitKey(0)
def compress(self, X):
n = X.shape[0]
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
# Construct nearest neighbour graph
G = np.zeros([n, n])
for i in range(n):
neighbours = np.argsort(D[i])[:self.nn + 1]
for j in neighbours:
G[i, j] = D[i, j]
G[j, i] = D[j, i]
# Compute ISOMAP distances
D = utils.dijkstra(G)
# If two points are disconnected (distance is Inf)
# then set their distance to the maximum
# distance in the graph, to encourage them to be far apart.
D[np.isinf(D)] = D[~np.isinf(D)].max()
# Initialize low-dimensional representation with PCA
pca = PCA(self.k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z, f = findMin(self._fun_obj_z, Z.flatten(), 500, D)
Z = z.reshape(n, self.k)
return Z
def compress(self, X):
n = X.shape[0]
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X,X)
D = np.sqrt(D)
#TODO:
D = self.construct_dist_graph(X , D)
# If two points are disconnected (distance is Inf)
# then set their distance to the maximum
# distance in the graph, to encourage them to be far apart.
D[np.isinf(D)] = D[~np.isinf(D)].max()
# Initialize low-dimensional representation with PCA
pca = PCA(self.k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z,f = findMin(self._fun_obj_z, Z.flatten(), 500, D)
Z = z.reshape(n, self.k)
return Z
def faceLoader() -> None:
'''
Face loader and visualizer example code
'''
gall = importGallery()
print(gall.shape)
gall = gall[:, :10]
print(gall.shape)
# Show first image
plt.figure(0)
plt.title('First face')
n = 0
nComponents = 10
pca = PCA(nComponents)
face = gall[:, :1]
print(face.shape)
# face = face.reshape(24576,1)
# print(face.shape)
mu, U, C, data = pca.train(gall)
alpha = pca.to_pca(data)
# print(alpha.shape)
# faceId = gall.item(n)[0][0]
# print('Face got face id: {}'.format(faceId))
face = alpha[:, :1]
print(face.shape)
face = face.reshape(192,128)
plt.imshow(face, cmap='gray')
plt.show()
def hack_pca(filename):
'''
Input: filename -- input image file name/path
Output: img -- image without rotation
'''
img_r = (plt.imread(filename)).astype(np.float64) # 4 channels: R,G,B,A
img_gray = img_r[:, :, 0] * 0.3 + img_r[:, :, 1] * 0.59 + img_r[:, :,
2] * 0.11
X_int = np.array(np.where(img_gray > 0))
X = X_int.astype(np.float64)
D, N = X.shape
eigen_vec, eigen_val = PCA(X)
print(eigen_vec, eigen_val)
Y = np.matmul(X.T, eigen_vec).T
Y_int = Y.astype(np.int32)
dmin = np.min(Y_int, axis=1).reshape(D, 1)
Y_int = Y_int - dmin
bound = np.max(Y_int, axis=1) + 1
new_img = np.zeros(bound)
for t in range(Y_int.shape[1]):
new_img[tuple(Y_int[:, t])] = img_gray[tuple(X_int[:, t])]
new_img = new_img.T[::-1, ::-1]
return new_img
def plot_iris(y, y_classes, maxit=25, *args, **kwargs):
# np.random.seed(0)
fig, ax = plot_grid(5)
#Variational bayes
vbpca = VBPCA(y, *args, **kwargs)
for i in range(maxit):
vbpca.update()
plot_scatter(vbpca.transform(), y_classes, ax[0])
ax[0].set_title('VBPCA')
#Laplace approximation
lbpca = LBPCA(y.T)
lbpca.fit(maxit)
plot_scatter(lbpca.transform(2).T, y_classes, ax[1])
ax[1].set_title('LBPCA')
#Streaming LBPCA
stream = create_distributed(np.copy(y.T), 10)
stream.randomized_fit(1)
plot_scatter(stream.transform(y.T, 2).T, y_classes, ax[2])
ax[2].set_title('Batch BPCA')
#Distributed LBPCA
stream = create_distributed(np.copy(y.T), 10)
stream.averaged_fit(maxit)
plot_scatter(stream.transform(y.T, 2).T, y_classes, ax[3])
ax[3].set_title('Parallel BPCA')
#PCA
pca = PCA(y.T)
plot_scatter(pca.fit_transform().T, y_classes, ax[4])
ax[4].set_title('PCA')
plt.show()
def toyExample():
mat = scipy.io.loadmat('../data/toy_data.mat')
data = mat['toy_data']
# TODO: Train PCA
pca = PCA(-1)
pca.train(data)
print("Variance of the data")
# TODO 1.2: Compute data variance to the S vector computed by the PCA
data_variance = np.var(data, axis=1)
print(data_variance)
print(np.power(pca.S, 2) / data.shape[1])
# TODO 1.3: Compute data variance for the projected data (into 1D) to the S vector computed by the PCA
Xout = pca.project(data, 1)
print("Variance of the projected data")
data_variance = np.var(Xout, axis=1)
print(data_variance)
print(np.power(pca.S[0], 2) / data.shape[1])
plt.figure()
plt.title('PCA plot')
plt.subplot(1, 2, 1) # Visualize given data and principal components
# TODO 1.1: Plot original data (hint, use the plot_pca function
pca.plot_pca(data)
plt.subplot(1, 2, 2)
# TODO 1.3: Plot data projected into 1 dimension
pca.S[1] = 0
pca.plot_pca(Xout)
plt.show()
def compress(self, X):
n = X.shape[0]
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
# D is symmetric matrix
geoD = np.zeros((n, n))
# find nn-neighbours
for i in range(n):
sort = np.argsort(D[:, i])
neigh = np.setdiff1d(sort[0:self.nn + 1], i)
# find the nn+1 smallest indexes that are not i
for j in range(len(neigh)):
t = neigh[j]
geoD[i, t] = D[i, t]
geoD[t, i] = D[t, i]
D = utils.dijkstra(geoD)
# for disconnected vertices (distance is Inf)
# set their dist = max_dist(graph)
# to encourage they are far away from each other
D[np.isinf(D)] = D[~np.isinf(D)].max()
# Initialize low-dimensional representation with PCA
pca = PCA(self.k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z, f = findMin(self._fun_obj_z, Z.flatten(), 500, D)
Z = z.reshape(n, self.k)
return Z
def get_update7(id):
entry_to_update = PCA_table.query.get_or_404(id)
if request.method == 'POST':
entry_to_update.clump = float(request.form['clump'])
entry_to_update.unifsize = float(request.form['unifsize'])
entry_to_update.unifshape = float(request.form['unifshape'])
entry_to_update.margadh = float(request.form['margadh'])
entry_to_update.singepisize = float(request.form['singepisize'])
entry_to_update.barenuc = float(request.form['barenuc'])
entry_to_update.blandchrom = float(request.form['blandchrom'])
entry_to_update.normnucl = float(request.form['normnucl'])
entry_to_update.mit = float(request.form['mit'])
payload = [
entry_to_update.clump, entry_to_update.unifsize,
entry_to_update.unifshape, entry_to_update.margadh,
entry_to_update.singepisize, entry_to_update.barenuc,
entry_to_update.blandchrom, entry_to_update.normnucl,
entry_to_update.mit
]
payload = np.array(payload)
entry_to_update.cell_class = PCA(payload)
try:
db.session.commit()
return redirect('/PCA')
except:
return 'Contact DBA'
else:
return render_template('PCA_update.html',
entry_to_update=entry_to_update)
def hullselect(self):
def selectHullPoints(data, n=20):
""" select data points for pairwise projections of the first n
dimensions """
# iterate over all projections and select data points
idx = np.array([])
# iterate over some pairwise combinations of dimensions
for i in combinations(range(n), 2):
# sample convex hull points in 2D projection
convex_hull_d = quickhull(data[i, :].T)
# get indices for convex hull data points
idx = np.append(idx, dist.vq(data[i, :], convex_hull_d.T))
idx = np.unique(idx)
return np.int32(idx)
# determine convex hull data points only if the total
# amount of available data is >50
#if self.data.shape[1] > 50:
pcamodel = PCA(self.data, show_progress=self._show_progress)
pcamodel.factorize()
idx = selectHullPoints(pcamodel.H, n=self._base_sel)
# set the number of subsampled data
self.nsub = len(idx)
return idx
def compress(self, X):
n = X.shape[0]
k = self.k
K = self.K
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
nbrs = np.argsort(D, axis=1)[:, 1:K + 1]
G = np.zeros((n, n))
for i in range(n):
for j in nbrs[i]:
G[i, j] = D[i, j]
G[j, i] = D[j, i]
D = utils.dijkstra(G)
D[D == np.inf] = -np.inf
max = np.max(D)
D[D == -np.inf] = max
# Initialize low-dimensional representation with PCA
Z = PCA(k).fit(X).compress(X)
# Solve for the minimizer
z = find_min(self._fun_obj_z, Z.flatten(), 500, False, D)
Z = z.reshape(n, k)
return Z
def hack_pca(filename):
'''
Input: filename -- input image file name/path
Output: img -- image without rotation
'''
img_r = (plt.imread(filename)).astype(np.float64) / 255
img_r = rgb2gray(img_r)
plt.imshow(img_r, cmap='gray')
plt.show()
m, n = img_r.shape
xy = []
xyv = []
for i in range(m):
for j in range(n):
if img_r[i, j] > 0:
xy.append((i, j))
xyv.append((i, j, img_r[i, j]))
xy = np.array(xy)
vector, value = PCA(xy)
d = np.array(np.round(np.dot(xy, vector))).astype(np.int)
min_xy = np.min(d, axis=0)
d -= min_xy
max_xy = np.max(d, axis=0)
img = np.zeros((max_xy[1] + 1, max_xy[0] + 1))
for i in range(xy.shape[0]):
img[max_xy[1] - d[i, 1], max_xy[0] - d[i, 0]] = xyv[i][2]
plt.imshow(img, cmap='gray')
plt.show()
return img
def compress(self, X):
n = X.shape[0]
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
sorted_indices = np.argsort(D)
G = np.zeros((n, n))
for i in range(D.shape[0]):
for j in range(self.nn + 1):
G[i, sorted_indices[i, j]] = D[i, sorted_indices[i, j]]
G[sorted_indices[i, j], i] = D[sorted_indices[i, j], i]
dist = utils.dijkstra(G)
dist[np.isinf(dist)] = dist[~np.isinf(dist)].max()
# Initialize low-dimensional representation with PCA
pca = PCA(self.k)
pca.fit(X)
Z = pca.compress(X)
# Solve for the minimizer
z, f = findMin(self._fun_obj_z, Z.flatten(), 500, dist)
Z = z.reshape(n, self.k)
return Z
def __init__(self, img_dataset: np.ndarray):
"""
:param img_dataset: An image dataset, which is a matrix with the shape of (N x H x W), where:
- N: number of images
- H: height of images
- W: width of images
- each item of the matrix is an real value between 0 and 1
Notes: All images should have same width and height
"""
# Get the shape of the input data
super().__init__()
assert len(img_dataset.shape) == 3
self._n_samples, self._height, self._width = img_dataset.shape
self.logger.info({
'msg': 'Image dataset shape',
'shape': img_dataset.shape
})
self._n_features = self._height * self._width
# Flatten the images of shape (height, width) to vectors of length height x width
self._flatten_dataset = img_dataset.reshape(
(self._n_samples, self._n_features))
# Build the PCA transformer
self._pca_transformer = PCA(self._flatten_dataset)
def hack_pca(filename, threshold=0.6):
'''
Input: filename -- input image file name/path
Output: img -- image without rotation
'''
img_r = (plt.imread(filename)).astype(np.float64) / 255
# YOUR CODE HERE
img = img_r[:, :, 0] * 0.299 + img_r[:, :, 1] * 0.587 + img_r[:, :,
2] * 0.114
H, W = img.shape
data = []
for i in range(H):
# x axis
for j in range(W):
# y axis
if img[i, j] >= threshold:
data.append([i, j])
data = np.array(data)
N = data.shape[0]
eigvectors, eigvalues = PCA(data)
(vx, vy) = eigvectors[:, 0]
(vx, vy) = (vx, vy) if vy >= 0 else (-vx, -vy)
theta = -math.asin(-vx) * 180 / math.pi
R = np.array([[vy, vx], [-vx, vy]]) # rotate matrix
odata = np.matmul(data, R)
odata -= np.min(odata, axis=0)
odata = odata.astype(int)
nH, nW = np.max(odata, axis=0)
oimg = np.zeros((nH + 1, nW + 1))
for i in range(N):
oimg[odata[i, 0], odata[i, 1]] = 1.
return img, oimg, theta
def compress(self, X):
n = X.shape[0]
# nearest_neighbours = np.zeros((n, self.nn))
# Compute Euclidean distances
D = utils.euclidean_dist_squared(X, X)
D = np.sqrt(D)
# If two points are disconnected (distance is Inf)
# then set their distance to the maximum
# distance in the graph, to encourage them to be far apart.
adjacency_matrix = np.zeros((n, n))
nearest_neighbours = self.knn(X)
for i, j in enumerate(nearest_neighbours):
for neighbour in j:
adjacency_matrix[i, neighbour] = D[i, neighbour]
adjacency_matrix[neighbour, i] = D[neighbour, i]
dijkstra = utils.dijkstra(adjacency_matrix)
dijkstra[np.isinf(dijkstra)] = dijkstra[~np.isinf(dijkstra)].max()
# Initialize low-dimensional representation with PCA
Z = PCA(self.k).fit(X).compress(X)
# Solve for the minimizer
z = find_min(self._fun_obj_z, Z.flatten(), 500, False, dijkstra)
Z = z.reshape(n, self.k)
return Z
def main():
# reduce the dimensionality of the data to two dimension and plot the results.
data = datasets.load_digits()
X = data.data
y = data.target
# Project the data onto the 2 primary principal components
X_trans = PCA().transform(X, 2)
x1 = X_trans[:, 0]
x2 = X_trans[:, 1]
cmap = plt.get_cmap('viridis')
colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(y)))]
class_distr = []
# Plot the different class distributions
for i, l in enumerate(np.unique(y)):
_x1 = x1[y == l]
_x2 = x2[y == l]
_y = y[y == l]
class_distr.append(plt.scatter(_x1, _x2, color=colors[i]))
plt.legend(class_distr, y, loc=1)
plt.suptitle("PCA Dimensionality Reduction")
plt.title("Digit Dataset")
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.show()
def train_models():
images, labels, labels_dic = collect_dat_set()
rec_eig = PCA(500, 5)
if images:
rec_eig.train(images, labels)
return rec_eig, labels_dic
def test_pca(self):
X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
pca = PCA(n_comp=2)
pca.fit(X)
self.assertEqual(
np.allclose(pca.explained_variance,
np.array([0.9924, 0.0075]),
atol=1e-3), True)
def testePCA(self):
pca = PCA()
matrizX = Matrizx()
idModelo = self.txtIdModelo.get()
matrizPrincipal = matrizX.selectMatrizXModeloMMM(idModelo)
pca.testePCA(matrizPrincipal)
def test_transform_inverse_transform(self):
X = self.data(500)
pca = PCA(X)
x = pca.transform(X, ndims=3)
self.assertTrue(
np.allclose(np.cov(x.T), np.eye(3), atol=1e-4, rtol=1e-2))
X_ = pca.inverse_transform(x)
self.assertTrue(np.allclose(X, X_))
def toyExample() -> None:
## Toy Data Set
mat = scipy.io.loadmat('../data/toy_data.mat')
data = mat['toy_data']
data = importGallery()
## limit datafor testing purposes
data = data[:, :144].T
print(data.shape)
## Iris dataset. Just for testing purposes
#iris = datasets.load_iris()
#data = iris['data'].astype(np.float32) # a 150x4 matrix with features
#data = data.T
# TODO: Train PCA
nComponents = 25
pca = PCA(nComponents)
## 1.1 Calculate PCA manuelly. SVD is following
#pca.pca_manuel(data)
## 1.2 Calculate PCA via SVD
mu, U, C, dataCenter = pca.train(data)
## 2. Transform RAW data using first n principal components
alpha = pca.to_pca(dataCenter)
## 3. Backtransform alpha to Raw data
Xout = pca.from_pca(alpha)
print("Variance")
# TODO 1.2: Compute data variance to the eigenvalue vector computed by the PCA
print(f'Total Variance: {np.var(data)}')
print(f'Eigenvalues: {C} \n')
# TODO 1.3: Compute data variance for the projected data (into 1D) to the S vector computed by the PCA
print(f'Total Variance Transform: {np.var(alpha)}')
print(f'Mean Eigenvalues: {np.mean(C)}')
## Plot only if fewer than 2 components
if nComponents == 2:
plt.figure()
plt.title('PCA plot')
plt.subplot(1, 2, 1) # Visualize given data and principal components
# TODO 1.1: Plot original data (hint, use the plot_pca function
pca.plot_pca(data)
plt.subplot(1, 2, 2)
# TODO 1.3: Plot data projected into 1 dimension
pca.plot_pca(Xout)
plt.show()
## Plot variances
else:
x = np.arange(1, len(C) + 1)
plt.bar(x, C)
plt.show()
def generate_pca_embedding_files():
'''
Generate PCA embedding csv files for the experiments.
'''
raw = genfromtxt('digits-raw.csv', delimiter=',')
X = raw[:, 2:]
pca = PCA(10)
X_new = pca.fit_transform(X)
raw_new = hstack((raw[:, :2], X_new))
savetxt('digits-pca-embedding.csv', raw_new, delimiter=',')
def test_invariance_to_many_transforms(self):
X = self.data(500)
pca = PCA(X)
x = pca.transform(X, ndims=2) # error introduced here
self.assertTrue(
np.allclose(np.cov(x.T), np.eye(2), atol=1e-4, rtol=1e-2))
X1 = pca.inverse_transform(x)
x1 = pca.transform(X1, ndims=2)
X2 = pca.inverse_transform(x1)
self.assertTrue(np.allclose(X1, X2))
def main(matrix, pcomps):
pca = PCA(pcomps, matrix)
# get covariance matrix - saved as instance var
pca.covariance_matrix()
# find evecs and evals of covariance matrix
pca.evecs_and_evals()
# get top x principal components aka evecs corresponding to top evals
pca.principal_components()
# reduce image on those components
return pca.get_evec_matrix()
def main():
whiten = False
if len(sys.argv) > 1 and sys.argv[1] == '--whiten':
whiten = True
del sys.argv[1]
if len(sys.argv) <= 3:
print 'Usage: %s pcaDims n_hidden learningRate' % sys.argv[0]
sys.exit(1)
# loads data like datasets = ((train_x, train_y), ([], None), (test_x, None))
datasets = loadUpsonData('../data/upson_rovio_1/train_15_50000.pkl.gz',
'../data/upson_rovio_1/test_15_50000.pkl.gz')
img_dim = 15 # must match actual size of training data
print 'done loading.'
pcaDims = int(sys.argv[1])
pca = PCA(datasets[0][0]) # train
datasets[0][0] = pca.toPC(datasets[0][0], pcaDims, whiten=whiten) # train
datasets[1][0] = pca.toPC(
datasets[1][0], pcaDims,
whiten=whiten) if len(datasets[1][0]) > 0 else array([]) # valid
datasets[2][0] = pca.toPC(datasets[2][0], pcaDims, whiten=whiten) # test
print 'reduced by PCA to'
print('(%d, %d, %d) %d dimensional examples in (train, valid, test)' %
(datasets[0][0].shape[0], datasets[1][0].shape[0],
datasets[2][0].shape[0], datasets[0][0].shape[1]))
# plot mean and principle components
image = Image.fromarray(
tile_raster_images(X=pca.meanAndPc(pcaDims).T,
img_shape=(img_dim, img_dim),
tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save(os.path.join(resman.rundir, 'meanAndPc.png'))
# plot fractional stddev in PCA dimensions
pyplot.semilogy(pca.fracStd, 'bo-')
if pcaDims is not None:
pyplot.axvline(pcaDims)
pyplot.savefig(os.path.join(resman.rundir, 'fracStd.png'))
pyplot.clf()
test_rbm(datasets=datasets,
training_epochs=45,
img_dim=img_dim,
n_input=pcaDims if pcaDims else img_dim * img_dim,
n_hidden=int(sys.argv[2]),
learning_rate=float(sys.argv[3]),
output_dir=resman.rundir,
quickHack=False,
visibleModel='real',
initWfactor=.01,
imgPlotFunction=lambda xx: pca.fromPC(xx, unwhiten=whiten))
