def slice_sample(init_x, logprob, sigma=1.0, step_out=True, max_steps_out=1000,
compwise=False, verbose=False):
def direction_slice(direction, init_x):
def dir_logprob(z):
return logprob(direction*z + init_x)
upper = sigma*npr.rand()
lower = upper - sigma
llh_s = np.log(npr.rand()) + dir_logprob(0.0)
l_steps_out = 0
u_steps_out = 0
if step_out:
while dir_logprob(lower) > llh_s and l_steps_out < max_steps_out:
l_steps_out += 1
lower -= sigma
while dir_logprob(upper) > llh_s and u_steps_out < max_steps_out:
u_steps_out += 1
upper += sigma
steps_in = 0
while True:
steps_in += 1
new_z = (upper - lower)*npr.rand() + lower
new_llh = dir_logprob(new_z)
if np.isnan(new_llh):
print new_z, direction*new_z + init_x, new_llh, llh_s, init_x, logprob(init_x)
raise Exception("Slice sampler got a NaN")
if new_llh > llh_s:
break
elif new_z < 0:
lower = new_z
elif new_z > 0:
upper = new_z
else:
raise Exception("Slice sampler shrank to zero!")
if verbose:
print "Steps Out:", l_steps_out, u_steps_out, " Steps In:", steps_in
return new_z*direction + init_x
if not init_x.shape:
init_x = np.array([init_x])
dims = init_x.shape[0]
if compwise:
ordering = range(dims)
npr.shuffle(ordering)
cur_x = init_x.copy()
for d in ordering:
direction = np.zeros((dims))
direction[d] = 1.0
cur_x = direction_slice(direction, cur_x)
return cur_x
else:
direction = npr.randn(dims)
direction = direction / np.sqrt(np.sum(direction**2))
return direction_slice(direction, init_x)
def __init__(self, size=None, n=None):
n = n if n else 256
self.size = size if size else (256, 256)
self.order = len(self.size)
# Generate WAY more numbers than we need
# because we are throwing out all the numbers not inside a unit
# sphere. Something of a hack but statistically speaking
# it should work fine... or crash.
G = (random.uniform(size=2*self.order*n)*2 - 1).reshape(-1, self.order)
# GAH! How do I generalize this?!
#length = hypot(G[:,i] for i in range(self.order))
if self.order == 1:
length = G[:,0]
elif self.order == 2:
length = hypot(G[:,0], G[:,1])
elif self.order == 3:
length = hypot(G[:,0], G[:,1], G[:,2])
self.G = (G[length < 1] / (length[length < 1])[:,newaxis])[:n,]
self.P = arange(n, dtype=int32)
random.shuffle(self.P)
self.idx_ar = indices(2*ones(self.order), dtype=int8).reshape(self.order, -1).T
self.drop = poly1d((-6, 15, -10, 0, 0, 1.0))
def random_inds(n, k):
"""Return k indices randomly from arange(n) in ascending order."""
tmp = arange(n)
shuffle(tmp)
inds = tmp[:k]
inds.sort()
return inds
def random_opt(varlist, init_list, dframe, print_out=False):
'''Optimize list by randomly adding variables,
accept if score decreases to find local minimum.'''
vlist = list(init_list)
# score, result = fit_train_score(vlist, dframe)
result, scores = do_kfold_cv(dframe, vlist, n_folds=10)
score = np.mean(scores)
if print_out:
print(" >>> iter init len %d, iter_score %.4f" % (len(vlist), score))
offset = len(vlist) # offset by length of initial vlist
indices = list(range(len(varlist) - offset))
rnd.shuffle(indices)
for ix in indices:
ilist = list(vlist)
ilist.append(varlist[ix + offset])
# iscore, iresult = fit_train_score(ilist, dframe)
iresult, iscores = do_kfold_cv(dframe, ilist, n_folds=10)
iscore = np.mean(iscores)
if print_out:
print(" >>> iter len %d, iter_score %.4f" % (len(ilist), iscore))
if iscore > score:
vlist = list(ilist)
result = iresult
scores = iscores
score = iscore
print(">>> try len %d, score %.4f" % (len(vlist), score))
print("vlist %s" % (vlist))
return score, vlist, result, scores
def main():
parser = argparse.ArgumentParser(description = 'Generate HITs for Amazon Mechnical Turk workers.')
parser.add_argument('-f', help = 'The mtk data source file.')
parser.add_argument('-o', help = 'The output file of used data.')
args = parser.parse_args()
data_sources = []
if (args.f != None):
data_sources = utils.load_file(args.f)
random.shuffle(data_sources)
db_collections = hit.setup_mongodb()
data_metainfo = hit.regex_datasource(data_sources)
images_metainfo = hit.query_imagedata_from_db(db_collections, data_metainfo)
# data_labels: flickr high interesting 1, flickr low interesting 2, pinterest [3, 4, 5]
data_labels = data_metainfo[0]
# data_ids: (flickr, pinterest) image id
data_ids = data_metainfo[1]
data_count_limit = 50
for begin_index in range(0, len(data_sources), data_count_limit):
print("index: " + str(begin_index))
generate_hits(data_sources[begin_index:begin_index + data_count_limit], begin_index, args, data_ids[begin_index:begin_index + data_count_limit], images_metainfo)
sys.exit(0)
def cv(model, mode, k_fold, X, Y):
"""
cross validation
parameters:
model: model object used for fitting
k_fold: number fold to devide the data
X: input data numpy arraya
Y: class label numpy array
"""
if len(X) % k_fold:
fold_size = int(len(X) / k_fold)
else:
fold_size = int(len(X) // k_fold)
indx = list(range(len(X)))
shuffle(indx)
test_indx = [indx[i: i + fold_size] for i in range(0, len(indx), fold_size)]
test_error = []
train_error = []
for fold in test_indx:
train_indx = list(set(indx) - set(fold))
X_train = X[train_indx]
Y_train = Y[train_indx]
X_test = X[fold]
Y_test = Y[fold]
fit = model(X_train, Y_train, mode)
train_error.append(fit.predict(X_train, Y_train)[1])
test_error.append(fit.predict(X_test, Y_test)[1])
return train_error, test_error
def subsample_otu_zero(otu, ss_fraction, zero_fraction):
"""Evenly subsample an OTU and randomly set zero_fraction entries = 0."""
ss_otu = subsample_otu_evenly(otu, ss_fraction)
inds = arange(len(ss_otu))
shuffle(inds)
ss_otu[inds[: int(len(inds) * zero_fraction)]] = 0
return ss_otu
def _reset(self, shuffle=True):
"""Resets the read pointer back to the beginning of the data set. If
``shuffle`` is set to True, also creates a new random order for
iterating the input lines.
:type shuffle: bool
:param shuffle: also shuffles the input sentences, unless set to False
"""
self._next_line = 0
if shuffle:
logging.debug("Generating a random order of input lines.")
samples = []
for (start, stop), sample_size in \
zip(self._sentence_pointers.pointer_ranges, self._sample_sizes):
population = numpy.arange(start, stop, dtype='int64')
# No duplicates, unless we need more sentences than there are
# in the file.
replace = sample_size > len(population)
sample = random.choice(population, sample_size, replace=replace)
samples.append(sample)
self._order = numpy.concatenate(samples)
for _ in range(10):
random.shuffle(self._order)
def random_opt(clf, varlist, init_list, loans_df, loans_y, score_fn=get_cv_score, rescale=True, print_out=False):
'''Optimize list by randomly adding variables,
accept if score decreases to find local minimum.'''
vlist = list(init_list)
score, vstd, vscores = score_fn(clf, vlist, loans_df, loans_y, rescale)
if print_out:
print(" >>> iter init len %d, iter_score %.4f" % (len(vlist), score))
offset = len(vlist) # offset by length of initial vlist
indices = list(range(len(varlist) - offset))
rnd.shuffle(indices)
for ix in indices:
ilist = list(vlist)
ilist.append(varlist[ix + offset])
iscore, istd, iscores = score_fn(clf, ilist, loans_df, loans_y, rescale)
if print_out:
print(" >>> iter len %d, iter_score %.4f" % (len(ilist), iscore))
if iscore > score:
vlist = list(ilist)
score, vstd, vscores = iscore, istd, iscores
print(">>> try len %d, score %.4f +- %.4f" % (len(vlist), score, 2 * vstd))
print("vlist %s" % (vlist))
# return dict ?
return score, vlist, vscores
def make_file_list(gtzan_path, n_folds=5,):
"""
Generates lists
"""
audio_path = os.path.join(gtzan_path,'audio')
out_path = os.path.join(gtzan_path,'lists')
files_list = []
for ext in ['.au', '.mp3', '.wav']:
files = U.getFiles(audio_path, ext)
files_list.extend(files)
random.shuffle(files_list)
if not os.path.exists(out_path):
os.makedirs(out_path)
audio_list_path = os.path.join(out_path, 'audio_files.txt')
open(audio_list_path,'w').writelines(['%s\n' % f for f in files_list])
annotations = get_annotations(files_list)
ground_truth_path = os.path.join(out_path, 'ground_truth.txt')
open(ground_truth_path,'w').writelines(generate_mirex_list(files_list, annotations))
generate_ground_truth_pickle(ground_truth_path)
folds = get_folds(files_list, n_folds=n_folds)
### Single fold for quick experiments
create_fold(0, 1, folds, annotations, out_path)
for n in range(n_folds):
create_fold(n, n_folds, folds, annotations, out_path)
def simple_num_driver():
print("Started at: " + str(datetime.datetime.now()))
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logger = logging.getLogger()
xs = []
maxlen = 100
max_features=maxlen + 1
from numpy.random import shuffle
r = range(1, maxlen + 1, 1)
for i in range(1000):
shuffle(r)
new_x = r[::]
xs.append(new_x)
ys = xs
xs = np.asarray(xs)
def to_one_hot(id):
zeros = [0] * max_features
zeros[id] = 1
return zeros
ys = map(lambda x: map(to_one_hot, x), ys)
ys = np.asarray(ys)
print("XS Shape: ", xs.shape)
print("YS Shape: ", ys.shape)
seq2seq(xs, ys, max_features, maxlen)
def pick_suitable_URL(outlinks):
'''
Given a list of URLs, find one (at random) and find all images, text
associated with that URL. If the text is suitably long, and there
are images, then output the URL, a list of images, and the text
'''
li = 0
lt = 0
random.shuffle(outlinks)
while (li < 1) | (lt < 400):
try:
thenewURL = outlinks.pop()
#print("PSU {}".format(thenewURL))
except:
# return empty variables if no suitable links are found
return '', [], []
else:
#print("Trying {}".format(thenewURL))
theTxt = get_text(thenewURL)
images = get_imgURLs(thenewURL)
images = ban_urls(images)
random.shuffle(images)
li = len(images)
lt = len(theTxt)
return thenewURL, images, theTxt
def batch_creating_template(self,
input_vals,
preprocess,
get_prev_elems,
get_elems,
add_input_to_batch):
batches = []
batch = [[] for i in xrange(self.n_inputs)]
input_vals = preprocess(input_vals)
prev_elems = get_prev_elems(input_vals)
for input_val in input_vals:
elems = get_elems(input_val)
n_samples = len(batch[-1])
if self._is_batch_boundary(elems, prev_elems, n_samples):
prev_elems = elems
batches.append(batch)
batch = [[] for i in xrange(self.n_inputs)]
batch = add_input_to_batch(batch, input_val)
if len(batch[0]) > 0:
batches.append(batch)
shuffle(batches)
return batches
def train(self, x, y):
# combine x,y into one dataset
data = np.zeros([len(x), len(x[0])+1])
data[:, 0:len(x[0])] = x
data[:, len(x[0])] = y
self.forest =[]
B = self.B
for b in range(0, B):
if self.Bagging:
self.count = 0
shuffle(data)
# bootstrapping: select 60% data points randomly
randomdata = data[:(len(x) * 0.6)]
# node of tree, [0]: split value, [1] split attribute, [2] left child ID, [3] right child ID
self.tree = np.zeros((randomdata.size, 4))
self.buildtree(randomdata)
# add the new tree to the forest
self.forest.append(self.tree[0:self.count+1])
self.tree = None
randomdata = None
else:
self.count = 0
self.tree = np.zeros((data.size, 4))
self.buildtree(data)
self.forest.append(self.tree[0:self.count+1])
self.tree = None
def __getVectorC(self, length):
# ensure at least 1/3 positive values
data = npr.randint(-10, 10, [length, 1])
posCount = np.ceil(length / 3)
data[:posCount, 0] = npr.randint(0, 10, posCount)
npr.shuffle(data)
return np.matrix(data)
def evolve(inital_gene_pop, ref_gene, generations):
"""Evolve a gene population to maximized graphic dissimilarity.
Convinience function with reduced number of params."""
# initial, generation 0 children
gene_children = [coerce_gene(gene, ref_gene) for gene in inital_gene_pop]
# make mutation variance generator
vg = var_gen(generations)
fitness_means = []
fitness_maxs = []
# run through generation number of selection cycles
for gen in range(generations):
# DEFINE ME
df_and_params = [gaussian, 0, vg.next()]
ec, cc, mc = selection(gene_children, ref_gene, df_and_params,
elite_children=.02, crossover_children=.8, mutation_children=.18,
fitness_function='graphic_dissimilarity')
# coerce cc and mc since elite children haven't been mutated or crossed
# so their summary stats are still the same.
cc = [coerce_gene(i, ref_gene) for i in cc]
mc = [coerce_gene(i, ref_gene) for i in mc]
gene_children = ec+cc+mc
# shuffle may be unnecessary
shuffle(gene_children)
tmp =[fitness(i,ref_gene) for i in gene_children]
fitness_means.append(mean(tmp))
fitness_maxs.append(max(tmp))
# final coerce step because gene_children have different summary stats
#res = [coerce_gene(i,ref_gene) for i in gene_children]
return gene_children, fitness_means, fitness_maxs
def main():
parser = argparse.ArgumentParser(description = 'Generate HITs for Amazon Mechnical Turk workers.')
parser.add_argument('-f', help = 'The mtk data source file.')
parser.add_argument('-o', help = 'The output file of used data.')
parser.add_argument('-m', default = 'normal', help = 'The running mode in {normal, qua_init, qua}.')
parser.add_argument('-q', help = 'The qualification type id.')
parser.add_argument('-t', default = 'sandbox', help = 'The type of Mechanical Turk.')
args = parser.parse_args()
if (args.m == 'qua' and args.q == None):
print('Please give qualification type id if running in qualification mode.')
sys.exit(0)
data_sources = []
if (args.f != None):
data_sources = utils.load_file(args.f)
if (args.m != 'qua'):
random.shuffle(data_sources)
data_count_limit = 100
for begin_index in range(0, len(data_sources), data_count_limit):
print("index: " + str(begin_index))
generate_hits(args.t, data_sources[begin_index:begin_index + data_count_limit], begin_index, args)
sys.exit(0)
def push_back_students(self):
more_pushing = True
students_to_kick = {}
while more_pushing:
more_pushing = False
self.sorted_section_indices.sort(key = lambda sec_ind: -self.section_capacities[sec_ind])
for sec_ind in self.sorted_section_indices:
students_to_push = numpy.transpose(numpy.nonzero((self.enroll_orig[:, sec_ind, self.section_schedules[sec_ind,:]].any(axis=1) * (True - self.enroll_final[:,:,self.section_schedules[sec_ind,:]].any(axis=(1,2))))))
more_pushing |= bool(len(students_to_push))
for student_ind in students_to_push:
self.enroll_final[student_ind, sec_ind, self.section_schedules[sec_ind,:]] = True
if self.section_capacities[sec_ind] > 0:
self.section_capacities[sec_ind] -= 1
else:
if not sec_ind in students_to_kick.keys():
students_to_kick[sec_ind] = numpy.transpose(numpy.nonzero((self.enroll_final*self.request)[:, sec_ind, self.section_schedules[sec_ind,:]].any(axis=1)))
pq = Queue.PriorityQueue()
random.shuffle(students_to_kick[sec_ind])
for [student] in students_to_kick[sec_ind]:
old_sections = numpy.transpose(numpy.nonzero(self.enroll_orig[student, :, self.section_schedules[sec_ind,:]].any(axis=1)))
if not len(old_sections):
pq.put((0, random.random(), student), False)
for [old_section] in old_sections:
pq.put((self.section_scores[old_section], random.random(), student), False)
students_to_kick[sec_ind] = pq
try:
self.enroll_final[students_to_kick[sec_ind].get(False)[2], sec_ind, self.section_schedules[sec_ind,:]] = False
except Queue.Empty:
pass
def take_random_ids(all_ids, num_to_take):
"""takes num_to_take ids from shuffled list of all_ids"""
if len(all_ids) < num_to_take:
raise ValueError('trying to take too many ids from all ids')
l = deepcopy(all_ids)
shuffle(l)
return l[:num_to_take]
def _mapCols(e, params):
from numpy import random
from disco.core import Params
m, n = params.m, params.n
output = []
if n > 0:
elems = e.split(",")
l = range(0, m)
random.shuffle(l)
for elem in elems:
retVal = []
j = int(elem)
nnz = m * (1.0 - params.sparsity)
stepSize = int(m / nnz)
k = int(random.random() * (m % nnz))
while k<m:
i = l[k]
k += stepSize
val = params.lb + (params.ub-params.lb) * random.random()
retVal.append("%d,%d,%.14f" % (i,j,val))
# break output into tuples so reduce can distribute the load
if len(retVal) > 1000:
output += [(";".join(retVal), "")]
retVal = []
if len(retVal) > 0:
output += [(";".join(retVal), "")]
return output
def circle_stats(circ):
npr.shuffle(circ)
npr.shuffle(circ.T)
sums = circ.sum(axis=0)
sums = np.sort(sums)
plt.plot(sums)
plt.savefig("./pics/circle_stats")
def time_varying_coefficients(d, timelines, constant=False, independent=0, randgen= random.exponential):
"""
Time vary coefficients
d: the dimension of the dataset
timelines: the observational times
constant: True for constant coefficients
independent: the number of coffients to set to 0 (covariate is ind of survival), or
a list of covariates to make indepent.
randgen: how scalar coefficients (betas) are sampled.
returns a matrix (t,d+1) of coefficients
"""
t = timelines.shape[0]
try:
a = np.arange(d)
random.shuffle(a)
independent = a[:independent]
except IndexError:
pass
n_funcs = len(FUNCS)
coefficients = np.zeros((t,d))
data_generators = []
for i in range(d):
f = FUNCS[random.randint(0,n_funcs)] if not constant else constant_
if i in independent:
beta = 0
else:
beta = randgen((1-constant)*0.5/d)
coefficients[:,i] = f(timelines,alpha=randgen(2000.0/t), beta=beta)
data_generators.append(f.func_doc)
df_coefficients = pd.DataFrame( coefficients, columns = data_generators, index = timelines)
return df_coefficients
def balanced_sample_maker(X, y, random_seed=None):
""" return a balanced data set by oversampling minority class
current version is developed on assumption that the positive
class is the minority.
Parameters:
===========
X: {numpy.ndarrray}
y: {numpy.ndarray}
"""
uniq_levels = unique(y)
uniq_counts = {level: sum(y == level) for level in uniq_levels}
if not random_seed is None:
random.seed(random_seed)
# find observation index of each class levels
groupby_levels = {}
for ii, level in enumerate(uniq_levels):
obs_idx = [idx for idx, val in enumerate(y) if val == level]
groupby_levels[level] = obs_idx
# oversampling on observations of positive label
sample_size = uniq_counts[0]
over_sample_idx = random.choice(groupby_levels[1], size=sample_size, replace=True).tolist()
balanced_copy_idx = groupby_levels[0] + over_sample_idx
random.shuffle(balanced_copy_idx)
return X[balanced_copy_idx, :], y[balanced_copy_idx]
def read_file(filename, n_fold):
data_sources = []
parts = []
part_of_data = utils.load_file(filename)
part_of_data = part_of_data[1:len(part_of_data)]
part_of_data = filter_content(part_of_data)
parts.append(part_of_data)
parts = [item for sublist in parts for item in sublist]
data_sources = array(parts)
random.shuffle(data_sources)
data_count_limit = len(data_sources) / n_fold
folds = []
count = 0
for begin_index in range(0, len(data_sources), data_count_limit):
end_index = begin_index + data_count_limit
if (count == n_fold - 1):
end_index = len(data_sources)
print("begin: " + str(begin_index))
print("end: " + str(end_index))
folds.append(data_sources[begin_index:end_index])
count += 1
return folds
def _get_random_field(self):
if self.seed == True:
np.random.seed(101)
'''simulates the Gaussian random field'''
# evaluate the eigenvalues and eigenvectors of the autocorrelation
# matrix
_lambda, phi = self.eigenvalues
# simulation points from 0 to 1 with an equidistant step for the LHS
randsim = linspace(0, 1, len(self.xgrid) + 1) - 0.5 / (len(self.xgrid))
randsim = randsim[1:]
# shuffling points for the simulation
shuffle(randsim)
# matrix containing standard Gauss distributed random numbers
xi = transpose(
ones((self.nsim, len(self.xgrid))) * array([norm().ppf(randsim)]))
# eigenvalue matrix
LAMBDA = eye(len(self.xgrid)) * _lambda
# cutting out the real part
ydata = dot(dot(phi, (LAMBDA) ** 0.5), xi).real
if self.distr_type == 'Gauss':
# scaling the std. distribution
scaled_ydata = ydata * self.stdev + self.mean
elif self.distr_type == 'Weibull':
# setting Weibull params
Pf = norm().cdf(ydata)
scaled_ydata = weibull_min(
self.shape, scale=self.scale, loc=self.loc).ppf(Pf)
self.reevaluate = False
rf = reshape(scaled_ydata, len(self.xgrid))
if self.non_negative_check == True:
if (rf < 0).any():
raise ValueError, 'negative value(s) in random field'
return rf
def examineExample(self, i): E = self.computeError(i); r = E*self.targetData[i]; if (r < -self.error and self.alpha[i] < self.C) or (r > self.error and self.alpha[i] > 0): # Heuristic for choosing second example # First heuristic: find the maximum |E1 - E2| j = -1 dE = 0 for k in xrange(self.numOfData): if fabs(self.computeError(k) - self.computeError(i)) > dE and i != k: dE = fabs(self.computeError(k) - self.computeError(i)) j = k if j != -1: if self.takeStep(i, j): return True # Second heuristic: amongst unbounded, try taking step # randomize index = [k for k in xrange(self.numOfData)] random.shuffle(index) for k in index: if self.isUnbounded(self.alpha[k]): if self.takeStep(i, k): return True random.shuffle(index) for k in index: if self.takeStep(i, k): return True return False
def load_data():
from numpy import random
data = matrix(genfromtxt('wavy_data.csv', delimiter=','))
random.shuffle(data)
data = asarray(data)
K = 3
data_train = (k_fold_cross_validation(data, K))[0]
data_test = (k_fold_cross_validation(data, K))[1]
xfull = data[:,0]
xfull.shape = (size(xfull),1)
yfull = data[:,1]
yfull.shape = (size(yfull),1)
xtrain = data_train[:,0]
xtrain.shape = (size(xtrain),1)
ytrain = data_train[:,1]
ytrain.shape = (size(ytrain),1)
xtest = data_test[:,0]
xtest.shape = (size(xtest),1)
ytest = data_test[:,1]
ytest.shape = (size(ytest),1)
return xfull, yfull, xtrain, ytrain, xtest, ytest
def bootstrap_compare(data1, data2, n_runs = 10000): # difference between 1 and 2: diff_1_2 = np.mean(data1) - np.mean(data2); print 'The difference between the means is %f' % diff_1_2 sz1 = len(data1); data = np.array(list(data1) + list(data2)); diffs = np.array([0.0] * n_runs); for r in range(n_runs): # shuffle the data to assume that they are from the same distribution: npr.shuffle(data); # split in two and determine the difference: d1 = data[:sz1]; d2 = data[sz1:]; diffs[r] = np.mean(d1) - np.mean(d2); # sort the array of differences and find the index of the first value bigger than diff_1_2: sorted_diffs = np.sort(diffs); index = n_runs; for r in range(n_runs): if(sorted_diffs[r] > diff_1_2): print 'found!' index = r; break; alpha = float(index) / n_runs; print 'The difference between the mean of data1 and data2 has an alpha of %f' % alpha pl.figure(); pl.hist(sorted_diffs, 30, facecolor=(0.7, 0.7,0.7)); pl.text(diff_1_2, 100, '*', color=(0,0,0), weight='bold')
def compare(imagesetlist,
ax=None, rows=5, cols=20, vlims=None, grid=True, random=False):
d = len(imagesetlist)
n_images = imagesetlist[0].shape[0]
imshape = imagesetlist[0].shape[1:]
m, n = imshape[:2]
n_channels = imshape[2] if len(imshape) > 2 else 1
inds = np.arange(n_images)
if random:
npr.shuffle(inds)
img_shape = (d*m*rows, n*cols)
if n_channels > 1:
img_shape = img_shape + (n_channels,)
img = np.zeros(img_shape, dtype=imagesetlist[0].dtype)
for ind in range(min(rows*cols, n_images)):
i,j = (ind / cols, ind % cols)
for k in xrange(d):
img[(d*i+k)*m:(d*i+k+1)*m, j*n:(j+1)*n] = \
imagesetlist[k][inds[ind],:].reshape(imshape)
ax = show(img, ax=ax, vlims=vlims)
if grid:
for i in xrange(1,rows):
ax.plot( [-0.5, img.shape[1]-0.5], (d*i*m-0.5)*np.ones(2), 'r-' )
for j in xrange(1,cols):
ax.plot( [j*n-0.5, j*n-0.5], [-0.5, img.shape[0]-0.5], 'r-')
ax.set_xlim([-0.5, img.shape[1]-0.5])
ax.set_ylim([-0.5, img.shape[0]-0.5])
ax.invert_yaxis()
def main():
inp = 'DO NOT USE PC'
div = 5
deck = ['CA', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'CJ', 'CQ', 'CK',
'DA', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'DJ', 'DQ', 'DK',
'HA', 'H2', 'H3', 'H4', 'H5', 'H6', 'H7', 'H8', 'H9', 'H10', 'HJ', 'HQ', 'HK',
'SA', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9', 'S10', 'SJ', 'SQ', 'SK',
'JA', 'JB']
nprnd.shuffle(deck)
print(deck)
# encryption
print('encoding')
split_list = divider(inp, div)
keystream = gen_keystream(len(''.join(split_list)), copy.deepcopy(deck), div)
print(split_list, keystream)
inp_num = letter2num(split_list)
key_num = letter2num(keystream)
encode_num = [(x+y)%26 for x,y in zip(inp_num, key_num)]
encode_num = [26 if x==0 else x for x in encode_num]
encoded = divider(''.join([chr(x+64) for x in encode_num]), div)
print(encoded)
print('')
# decryption
print('decoding')
keystream = gen_keystream(len(''.join(encoded)), copy.deepcopy(deck), div)
print(encoded, keystream)
inp_num = letter2num(encoded)
key_num = letter2num(keystream)
decode_num = [(x-y)%26 for x,y in zip(inp_num, key_num)]
decode_num = [26 if x==0 else x for x in decode_num]
decoded = divider(''.join([chr(x+64) for x in decode_num]), div)
print(decoded)
from pickle import dump
from numpy.random import rand
from numpy.random import shuffle
# load a clean dataset
def load_clean_sentences(filename):
return load(open(filename, 'rb'))
# save a list of clean sentences to file
def save_clean_data(sentences, filename):
dump(sentences, open(filename, 'wb'))
print('Saved: %s' % filename)
# load dataset
raw_dataset = load_clean_sentences('english-german.pkl')
# reduce dataset size
n_sentences = 10000
dataset = raw_dataset[:n_sentences, :]
# random shuffle
shuffle(dataset)
# split into train/test
train, test = dataset[:9000], dataset[9000:]
# save
save_clean_data(dataset, 'english-german-both.pkl')
save_clean_data(train, 'english-german-train.pkl')
save_clean_data(test, 'english-german-test.pkl')
category = -1.0
elif (x % 2) == (y % 2):
category = 1.0
else:
category = -1.0
xvals = random.normal(xNorm, 0.05, numPoints)
yvals = random.normal(yNorm, 0.05, numPoints)
for i in range(numPoints): # xrange in Python 2
catData.append((xvals[i], yvals[i], category))
catData = array(catData)
random.shuffle(catData)
knowns = catData[:, -1]
data = catData[:, :-1]
params = (0.1, )
supports, steps, kernelArray = svmTrain(knowns, data, kernelGauss, params)
score = svmSeparation(knowns, supports, kernelArray)
print('Known data: %5.2f%% correct' % (score))
print("\nSupport vector machine prediction boundaries\n")
ds1x = []
ds1y = []
ds2x = []
#程序文件Pex17_2.py
import numpy as np
from numpy.random import randint, rand, shuffle
from matplotlib.pyplot import plot, show, rc
a=np.loadtxt("Pdata17_2.txt")
xy,d=a[:,:2],a[:,2:]; N=len(xy)
w=50; g=10 #w为种群的个数，g为进化的代数
J=[];
for i in np.arange(w):
c=np.arange(1,N-1); shuffle(c)
c1=np.r_[0,c,101]; flag=1
while flag>0:
flag=0
for m in np.arange(1,N-3):
for n in np.arange(m+1,N-2):
if d[c1[m],c1[n]]+d[c1[m+1],c1[n+1]]<\
d[c1[m],c1[m+1]]+d[c1[n],c1[n+1]]:
c1[m+1:n+1]=c1[n:m:-1]; flag=1
c1[c1]=np.arange(N); J.append(c1)
J=np.array(J)/(N-1)
for k in np.arange(g):
A=J.copy()
c1=np.arange(w); shuffle(c1) #交叉操作的染色体配对组
c2=randint(2,100,w) #交叉点的数据
for i in np.arange(0,w,2):
temp=A[c1[i],c2[i]:N-1] #保存中间变量
A[c1[i],c2[i]:N-1]=A[c1[i+1],c2[i]:N-1]
A[c1[i+1],c2[i]:N-1]=temp
B=A.copy()
by=[] #初始化变异染色体的序号
while len(by)<1: by=np.where(rand(w)<0.1)
def shuffle(self):
self.__init__()
shuffle(self.__set)
# fake_pred = torch.rand(1, GRID_NUM, GRID_NUM, 30)
# decoder(fake_pred)
CONTINUE = False # continue from breakpoint
model = Yolov1(backbone_name='resnet50')
model.load_model()
# predict_one_img('../test_img/000001.jpg', model)
# test_img_dir = '../test_img'
test_img_dir = '/Users/chenlinwei/Dataset/VOC0712/VOC2012test/JPEGImages'
for root, dirs, files in os.walk(test_img_dir, topdown=True):
if test_img_dir == root:
print(root, dirs, files)
files = [
i for i in files
if any([j in i for j in ['jpg', 'png', 'jpeg', 'gif', 'tiff']])
]
shuffle(files)
if CONTINUE:
with open(osp.join(test_img_dir, 'tested.txt'), 'a') as _:
pass
with open(osp.join(test_img_dir, 'tested.txt'), 'r') as txt:
txt = txt.readlines()
txt = [i.strip() for i in txt]
print(txt)
files = [i for i in files if i not in txt]
for file in files:
file_path = os.path.join(root, file)
print(f'*** testing:{file_path}')
predict_one_img(file_path, model)
with open(osp.join(test_img_dir, 'tested.txt'),
'a') as txt:
txt.write(file + '\n')
monitor='Real_Monitor', color=[0,0,0], colorSpace='rgb',
blendMode='avg', useFBO=True)
# store frame rate of monitor if we can measure it
expInfo['frameRate'] = win.getActualFrameRate()
if expInfo['frameRate'] != None:
frameDur = 1.0 / round(expInfo['frameRate'])
else:
frameDur = 1.0 / 60.0 # could not measure, so guess
# Initialize components for Routine "Task_Start"
Task_StartClock = core.Clock()
changetime = 12
nochangetime = 3.14
imagenumarray =['0001','0002','0004','0005','0006','0008','0009','0011','0014','0016','0018','0019','0022','0023','0024','0028','0029','0031','0038','0039','0040','0041','0044','0045','0048','0049','0050','0051','0052','0054','0055','0056','0058','0060','0066','0067','0069','0070','0071','0074','0075','0076','0078','0079','0080','0084','0087','0089','0093','0097','0098','0099','0100','0102','0106','0107','0109','0110','0111','0112','0114','0116','0117','0118','0119','0122','0123','0124','0125','0126','0127','0128']
shuffle(imagenumarray)
Task_Instructions = visual.TextStim(win=win, name='Task_Instructions',
text="In this task you will be presented with photographs of various scenes. In some of these scenes, a specific object in the scene may periodically appear or disappear several times in a row. Your task is to press the 'spacebar' key AS SOON AS you see such a change occuring. After making that response, you will be asked to left-click on where the change occured with the mouse. Do remember to press the spacebar AS SOON as you actually see the change though as that response is what will determine your score! It is not important to click on the location quickly but it is important to press the spacebar as soon as you see a change. Press the spacebar to see a few examples of these changes.",
font='Arial',
pos=(0, 0), height=0.075, wrapWidth=None, ori=0,
color=[-1.000,-1.000,-1.000], colorSpace='rgb', opacity=1,
depth=-1.0);
# Initialize components for Routine "Examples"
ExamplesClock = core.Clock()
ExampleImage1 = visual.ImageStim(
win=win, name='ExampleImage1',units='pix',
image='sin', mask=None,
ori=0, pos=(0, 0), size=(1024, 768),
color=[1,1,1], colorSpace='rgb', opacity=1,
flipHoriz=False, flipVert=False,
dim = 64
k = 4 # downscale size
gene_l1_factor = 0.9
beta1 = 0.5
learning_rate_start = 0.00020
learning_rate_half_life = 5000
dataset = '/home/data/houruibing/CelebA/img_align_celeba'
test_vector = 16 # num of test images
checkpoint_period = 10000
summary_period = 200
train_time = 60 * 5 # time in minutes to train the model
#prepare data
filenames = tf.gfile.ListDirectory(dataset)
filenames = sorted(filenames)
random.shuffle(filenames)
filenames = [os.path.join(dataset, f) for f in filenames]
train_filenames = filenames[: -test_vector]
test_filenames = filenames[-test_vector: ]
#checkpoint_dir = 'checkpoint/CelebA'
#if not os.path.exists(checkpoint_dir):
# os.makedirs(checkpoint_dir)
#train_dir = 'generate_images/CelebA'
#if not os.path.exists(train_dir):
# os.makedirs(train_dir)
checkpoint_dir = 'checkpoint/CelebA'
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
def sokoban_layout(limit = 1000, egocentric = False, objects = True, stage=0, test=False):
assert objects
list = ["sokoban_layout",limit,
("egocentric" if egocentric else "global"),
("object" if objects else "global"),
stage,
("test" if test else "train"),]
path = os.path.join(latplan.__path__[0],"puzzles","-".join(map(str,list))+".npz")
import gym
import pddlgym
import imageio
pre_layouts = []
suc_layouts = []
if egocentric:
layout_mode = "egocentric_layout"
else:
layout_mode = "layout"
env = gym.make("PDDLEnvSokoban-v0" if not test else "PDDLEnvSokobanTest-v0")
env.fix_problem_index(stage)
init, _ = env.reset()
init_layout = env.render(mode=layout_mode)
# reachability analysis
player = (init_layout == pddlgym.rendering.sokoban.PLAYER)
wall = (init_layout == pddlgym.rendering.sokoban.WALL)
reachable = compute_reachability_sokoban(wall,player)
relevant = np.maximum(reachable, wall)
print(f"{wall.sum()} wall objects:")
print(wall)
print(f"{reachable.sum()} reachable objects:")
print(reachable)
print(f"{relevant.sum()} relevant objects:")
print(relevant)
relevant = relevant.reshape(-1)
def successor(obs):
env.set_state(obs)
for action in env.action_space.all_ground_literals(obs, valid_only=True):
env.set_state(obs)
obs2, _, _, _ = env.step(action)
yield obs2
max_g = 0
for obs, close_list in dijkstra(init, float("inf"), successor, include_nonleaf=True, limit=limit):
max_g = max(max_g,close_list[obs]["g"])
pobs = close_list[obs]["parent"]
if pobs is None:
continue
env.set_state(pobs)
pre_layouts.append(env.render(mode=layout_mode))
env.set_state(obs)
suc_layouts.append(env.render(mode=layout_mode))
pre_layouts = np.array(pre_layouts)
suc_layouts = np.array(suc_layouts)
print(pre_layouts.shape)
print("max",pre_layouts.max(),"min",pre_layouts.min())
B, H, W = pre_layouts.shape
pre_layouts = pre_layouts.reshape((B,H*W))
suc_layouts = suc_layouts.reshape((B,H*W))
# shuffling
random_indices = np.arange(len(pre_layouts))
nr.shuffle(random_indices)
pre_layouts = pre_layouts[random_indices]
suc_layouts = suc_layouts[random_indices]
tile = 16
bboxes = tiled_bboxes(B, H, W, tile)
if not egocentric:
pre_layouts = pre_layouts[:,relevant]
suc_layouts = suc_layouts[:,relevant]
bboxes = bboxes[:,relevant]
# make it into a one-hot repr
eye = np.eye(pddlgym.rendering.sokoban.NUM_OBJECTS)
# B, H, W, C
pre_classes = eye[pre_layouts]
suc_classes = eye[suc_layouts]
print(pre_classes.shape)
np.savez_compressed(path,pres=pre_classes,sucs=suc_classes,bbox=bboxes,picsize=[H*tile,W*tile,3],max_g=max_g)
def shuffle(self):
shuffle(self.data)
N = len(z_avg)
indices = np.array(range(N))
quota = 0.60
tests = 2000
q_max = int(0.21 * quota * N)
err_avg = np.zeros(q_max - 1)
q_vec = np.array(range(1, q_max))
for k in range(tests):
if k % 100 == 0:
print("k = " + str(k))
rng.shuffle(indices)
ind_train = indices[:int(quota * N)]
ind_valid = indices[int(quota * N):]
"""
ind_train = indices[int(0.5*(1-quota)*N):N-int(0.5*(1-quota)*N)]
ind_valid = np.concatenate((indices[:int(0.5*(1-quota)*N)],indices[N-int(0.5*(1-quota)*N):]))
"""
err = []
for q in range(1, q_max):
p = np.polyfit(z_avg[ind_train], x_avg[ind_train], q)
x_hat = np.polyval(p, z_avg)
err.append(np.sqrt(np.sum((x_hat[ind_valid] - x_avg[ind_valid])**2)))
err_avg += np.array(err)
err_avg /= tests
def on_epoch_end(self):
self.indexes = arange(len(self.imagePaths))
if self.shuffle:
shuffle(self.indexes)
def sokoban_image(limit = 1000, egocentric = False, objects = True, stage=0, test=False):
list = ["sokoban_image",limit,
("egocentric" if egocentric else "global"),
("object" if objects else "global"),
stage,
("test" if test else "train"),]
path = os.path.join(latplan.__path__[0],"puzzles","-".join(map(str,list))+".npz")
import gym
import pddlgym
import imageio
pre_images = []
suc_images = []
if egocentric:
image_mode = "egocentric_crisp"
else:
image_mode = "human_crisp"
env = gym.make("PDDLEnvSokoban-v0" if not test else "PDDLEnvSokobanTest-v0")
env.fix_problem_index(stage)
init, _ = env.reset()
init_layout = env.render(mode="layout")
# reachability analysis
player = (init_layout == pddlgym.rendering.sokoban.PLAYER)
wall = (init_layout == pddlgym.rendering.sokoban.WALL)
reachable = compute_reachability_sokoban(wall,player)
relevant = np.maximum(reachable, wall)
print(f"{wall.sum()} wall objects:")
print(wall)
print(f"{reachable.sum()} reachable objects:")
print(reachable)
print(f"{relevant.sum()} relevant objects:")
print(relevant)
relevant = relevant.reshape(-1)
def successor(obs):
env.set_state(obs)
for action in env.action_space.all_ground_literals(obs, valid_only=True):
env.set_state(obs)
obs2, _, _, _ = env.step(action)
yield obs2
pairs = []
max_g = 0
for obs, close_list in dijkstra(init, float("inf"), successor, include_nonleaf=True, limit=limit):
max_g = max(max_g,close_list[obs]["g"])
pobs = close_list[obs]["parent"]
if pobs is None:
continue
pairs.append((pobs,obs))
threads = 16
pairss = []
len_per_thread = 1+(len(pairs) // threads)
for i in range(threads):
pairss.append(pairs[i*len_per_thread:(i+1)*len_per_thread])
from multiprocessing import Pool
with Pool(threads) as p:
for sub in tqdm.tqdm(p.imap(render_sokoban,
zip(pairss,
[image_mode]*threads))):
pre_images_sub = sub[0]
suc_images_sub = sub[1]
pre_images.extend(pre_images_sub)
suc_images.extend(suc_images_sub)
pre_images = np.array(pre_images)
suc_images = np.array(suc_images)
print(pre_images.shape)
print("max",pre_images.max(),"min",pre_images.min())
# shuffling
random_indices = np.arange(len(pre_images))
nr.shuffle(random_indices)
pre_images = pre_images[random_indices]
suc_images = suc_images[random_indices]
if not objects:
# whole image
np.savez_compressed(path,pres=pre_images,sucs=suc_images)
return
# image
tile = 16
B, H, W, C = pre_images.shape
pre_images = image_to_tiled_objects(pre_images, tile)
suc_images = image_to_tiled_objects(suc_images, tile)
bboxes = tiled_bboxes(B, H//tile, W//tile, tile)
print(pre_images.shape,bboxes.shape)
# prune the unreachable regions
if not egocentric:
pre_images = pre_images[:,relevant]
suc_images = suc_images[:,relevant]
bboxes = bboxes[:,relevant]
print(pre_images.shape,bboxes.shape)
# note: bbox can be reused for pres and sucs
picsize = [H,W,C]
np.savez_compressed(path,pres=pre_images,sucs=suc_images,bboxes=bboxes,picsize=picsize,max_g=max_g)
def alpha_beta_move(board, turn, depth = 0, alpha = (-inf,-inf), beta = (inf,inf), evaluation = lambda x: 0):
dummy_board = np.copy(board) # we don't want to change the board state
swap_player = {1:-1,-1:1} # So we can change whose turn
options = cccc.available_moves(board) # get legal moves
random.shuffle(options) # should inherit move order instead of randomizing
# if len(options) == 1:
# update_move(board,options[0])
# if cccc.winner(dummy_board):
# return (inf,options[0])
# else:
# return (0,options[0])
best_value = (-inf,-inf)
if not options:
print board, cccc.game_over(board)
print 'oops, no available moves'
cand_move = options[0]
if depth == 0:
for x in options:
update_move(dummy_board,x,turn)
op_value = (evaluation(dummy_board*swap_player[turn]) , depth)
if tuple(-1 * el for el in op_value) > best_value:
cand_move = x
best_value = tuple(-1 * el for el in op_value)
alpha = max(alpha, best_value)
# print depth,-op_value, best_value, cand_move,alpha,beta
if alpha >= beta:
# print 'pruned'
break #alpha-beta cutoff
unupdate_move(dummy_board,x)
else:
for x in options:
# dummy_board = np.copy(board)
# height= np.where(board[:,x]==0)[0][-1] #connect four only
# dummy_board[height, x] = turn
update_move(dummy_board,x,turn)
if cccc.winner(dummy_board): #should check over and tied too
return((inf,depth), x)
if cccc.is_full(dummy_board): #This assumes you can't lose on your turn
return((0,depth) , x)
op_value,_ = alpha_beta_move( dummy_board,
swap_player[turn],
depth-1,
alpha = tuple(-1 * el for el in beta),
beta = tuple(-1 * el for el in alpha),
evaluation = evaluation)
if tuple(-1 * el for el in op_value) > best_value:
cand_move = x
best_value = tuple(-1 * el for el in op_value)
alpha = max(alpha, best_value)
# print depth,-op_value, best_value, cand_move,alpha,beta
if alpha >= beta:
# print 'pruned'
break #alpha-beta cutoff
unupdate_move(dummy_board,x)
# dummy_board[height, x] = 0
return (best_value, cand_move)
def load(relativePath, labels, n=None, validationPercentage=None):
filenameImagesTrain = list(glob(join(relativePath, 'Train', 'Images',
'*')))
pathListLabelsTrain = []
for l in labels:
pathListLabelsTrain.append(join(relativePath, 'Train', 'Labels', l))
filenameImagesValidation = list(
glob(join(relativePath, 'Validation', 'Images', '*')))
pathListLabelsValidation = []
for l in labels:
pathListLabelsValidation.append(
join(relativePath, 'Validation', 'Labels', l))
if n is None:
dataTrain = []
for i in range(len(filenameImagesTrain)):
data = []
data.append(filenameImagesTrain[i])
for l in pathListLabelsTrain:
data.append(
list(
glob(
join(
l, ''.join(
(filenameImagesTrain[i].split(sep)[-1]
).split('.')[:-1]) + '.*')))[0])
dataTrain.append(data)
dataValidation = []
if validationPercentage is None:
for i in range(len(filenameImagesValidation)):
data = []
data.append(filenameImagesValidation[i])
for l in pathListLabelsValidation:
data.append(
list(
glob(
join(
l, ''.join(
(filenameImagesValidation[i].split(sep)
[-1]).split('.')[:-1]) + '.*')))[0])
dataValidation.append(data)
else:
for i in range(int(
len(filenameImagesTrain) * validationPercentage)):
data = []
data.append(filenameImagesValidation[i])
for l in pathListLabelsValidation:
data.append(
list(
glob(
join(
l, ''.join(
(filenameImagesValidation[i].split(sep)
[-1]).split('.')[:-1]) + '.*')))[0])
dataValidation.append(data)
else:
if validationPercentage is None:
validationPercentage = 0.5
nTrain = int(n * (1.0 - validationPercentage) /
len(filenameImagesTrain))
nValidation = int(n * validationPercentage /
len(filenameImagesValidation))
dataTrain = []
for i in range(nTrain):
data = []
data.append(filenameImagesTrain[i])
for l in pathListLabelsTrain:
data.append(
list(
glob(
join(
l, ''.join(
(filenameImagesTrain[i].split(sep)[-1]
).split('.')[:-1]) + '.*')))[0])
dataTrain.append(data)
dataValidation = []
for i in range(nValidation):
data = []
data.append(filenameImagesValidation[i])
for l in pathListLabelsValidation:
data.append(
list(
glob(
join(
l, ''.join(
(filenameImagesValidation[i].split(sep)[-1]
).split('.')[:-1]) + '.*')))[0])
dataValidation.append(data)
shuffle(dataTrain)
shuffle(dataValidation)
def splitLabelsImages(data, labels):
dataSplit = hsplit(asarray(data), array(range(1, len(labels) + 1)))
images = dataSplit[0]
labels = dataSplit[1:]
return images, labels
trainImages, trainLabels = splitLabelsImages(dataTrain, labels)
validationImages, validationLabels = splitLabelsImages(
dataValidation, labels)
return trainImages, trainLabels, validationImages, validationLabels
def train(g_model,
d_model,
gan_model,
dataset,
latent_dim,
n_epochs=100,
n_batch=128):
itr = 0
bat_per_epo = int(dataset.shape[0] / n_batch)
half_batch = int(n_batch / 2)
n_steps = bat_per_epo * n_epochs
# manually enumerate epochs
for i in range(n_steps):
# enumerate batches over the training set
for j in range(n_critic):
itr += 1
# get randomly selected 'real' samples
X_real, y_real = generate_real_samples(dataset, half_batch)
# update discriminator model weights
d_loss1 = d_model.train_on_batch(X_real, y_real)
# generate 'fake' examples
X_fake, y_fake = generate_fake_samples(g_model, latent_dim,
half_batch)
# update discriminator model weights
d_loss2 = d_model.train_on_batch(X_fake, y_fake)
if (itr % 1000 == 0):
(_, _), (images1, _) = cifar10.load_data()
shuffle(images1)
images1 = images1[:1000]
x_fake, y_fake = generate_fake_samples(g_model,
latent_dim,
n_samples=1000)
images2 = x_fake
images2 = (images2 + 1) / 2
images2 = 255 * images2
print('Loaded', images1.shape, images2.shape)
# convert integer to floating point values
images1 = images1.astype('float32')
images2 = images2.astype('float32')
# resize images
images1 = scale_images(images1, (299, 299, 3))
images2 = scale_images(images2, (299, 299, 3))
print('Scaled', images1.shape, images2.shape)
# pre-process images
images1 = preprocess_input(images1)
images2 = preprocess_input(images2)
# calculate fid
fid = calculate_fid(model_in, images1, images2)
print('FID: %.3f' % fid)
fid_score.append(fid)
epoch_arr.append(i)
# prepare points in latent space as input for the generator
X_gan = generate_latent_points(latent_dim, n_batch)
# create inverted labels for the fake samples
y_gan = ones((n_batch, 1))
# update the generator via the discriminator's error
g_loss = gan_model.train_on_batch(X_gan, y_gan)
# summarize loss on this batch
print('>%d, %d/%d, d1=%.3f, d2=%.3f g=%.3f' %
(i + 1, j + 1, bat_per_epo, d_loss1, d_loss2, g_loss))
# evaluate the model performance, sometimes
if (i + 1) % 500 == 0:
summarize_performance(i, g_model, d_model, dataset, latent_dim)
if expInfo['frameRate']!=None:
frameDur = 1.0/round(expInfo['frameRate'])
else:
frameDur = 1.0/60.0 # couldn't get a reliable measure so guess
# Initialize components for Routine "trial"
trialClock = core.Clock()
ISI = core.StaticPeriod(win=win, screenHz=expInfo['frameRate'], name='ISI')
# replace these list items with whatever your filename conventions are:
colors = ['yellow', 'white', 'orange', 'magenta', 'green', 'gray', 'cyan', 'blue']
shapes = ['triangle', 'square', 'line', 'invertedTriangle', 'hexagon', 'diamond', 'cross', 'circle']
rewards = [0.5, 1, 2, 4] * 2
conditions = ['go', 'go', 'go', 'go', 'stop', 'stop', 'stop', 'stop']
trialDetailsList = []
shuffle(colors)
shuffle(shapes)
for i, color in enumerate(colors): # cycle through each color and keep track of an index number
trialDetails = {} # a dictionary of key-value pairs
trialDetails['fileName'] = shapes[i] + color + '.gif'
trialDetails['reward'] = rewards[i]
trialDetails['condition'] = conditions[i]
trialDetailsList.append(trialDetails)
shuffle(trialDetailsList) # do this now to ensure that order of presentation of rewards and conditions is also shuffled
ConditionOne = trialDetailsList[0]
ConditionTwo = trialDetailsList[1]
ConditionThree = trialDetailsList[2]
ConditionFour = trialDetailsList[3]
seed(213) # DON'T CHANGE
### YOUR CODE HERE ###
mean_winnings = 0
num_trials = 1000000
# This sets the feedback interval so we know the program hasn't crashed.
feedback = int(np.round(num_trials / 10))
earns_money = 0
# red : 1, green : 2; blue : 3
red = [1] * 60
green = [2] * 30
blue = [3] * 10
balls = red + green + blue
shuffle(balls)
for t in range(1, num_trials + 1):
# To see the progress.
if t % feedback == 0:
print(np.round(100 * t / num_trials, 1),
'% complete: earned money expectation =', earns_money / t)
indices = np.random.choice(100, 3, replace=False)
chosen = [balls[indices[0]], balls[indices[1]], balls[indices[2]]]
red_num = chosen.count(1)
toll = randint(1, 7) + randint(1, 7)
if red_num > 1:
earns_money += toll
else:
earns_money -= toll
def init_event(self, event_file, first_init = False):
if self.verbose > 0:
print('\n\nLoading event:', event_file, '\n\n')
self.hits, self.cells, self.particles, self.truth = load_event(event_file)
self.hits_xyz = self.hits.values[:,1:4]
self.hits_r = np.sqrt(np.power(self.hits_xyz[:,0],2)+np.power(self.hits_xyz[:,1],2))
self.hits_phi_x = np.sign(self.hits_xyz[:,1]) * np.arccos(self.hits_xyz[:,0] / self.hits_r)
self.hits_phi_y = np.sign(-self.hits_xyz[:,0]) * np.arccos(self.hits_xyz[:,1] / self.hits_r)
self.hits_theta = np.arctan2(self.hits_xyz[:,2], self.hits_r)
self.hits_xyzrphiphitheta = np.concatenate((
self.hits_xyz,
np.reshape(self.hits_r,(-1,1)),
np.reshape(self.hits_phi_x,(-1,1)),
np.reshape(self.hits_phi_y,(-1,1)),
np.reshape(self.hits_theta,(-1,1))),
axis=1)
if self.std_scale:
if first_init:
self.coord_rescaler = preprocessing.StandardScaler().fit(self.hits_xyzrphiphitheta)
self.hits_xyzrphiphitheta = self.coord_rescaler.transform(self.hits_xyzrphiphitheta)
hits_module_array = self.hits.values[:,4:]
self.hits_module = []
for module in hits_module_array:
self.hits_module.append(tuple(module))
# Collect all ids except for id 0.
self.track_unique_ids = np.unique(np.append([0],self.truth.values[:,1]))
self.track_unique_ids = self.track_unique_ids[1:]
rand.shuffle(self.track_unique_ids)
self.ntracks = len(self.track_unique_ids)
self.ntrack_iter = 0
# Preprocess tracks
self.tracks = {}
self.track_hits = {}
self.track_coords = {}
self.track_hits_vols = {}
self.track_mod_onehot = {}
for track_unique_id in self.track_unique_ids:
hit_rows = self.truth['particle_id'] == track_unique_id
self.tracks[track_unique_id] = self.truth[hit_rows].drop('particle_id',axis=1)
self.track_hits[track_unique_id] = self.tracks[track_unique_id]['hit_id'].values[:]
self.track_coords[track_unique_id] = []
self.track_hits_vols[track_unique_id] = []
self.track_mod_onehot[track_unique_id] = []
for hit_id in np.nditer(self.track_hits[track_unique_id]):
# Append to track_coords array
self.track_coords[track_unique_id].append(self.hits_xyzrphiphitheta[hit_id-1])
# Find volumes of hits
self.track_mod_onehot[track_unique_id].append(self.onehot_module[self.hits_module[hit_id-1]])
# Pad track_vol_onehot
if len(self.track_hits[track_unique_id]) > 1:
self.track_mod_onehot[track_unique_id] = self.track_mod_onehot[track_unique_id][1:]
self.track_mod_onehot[track_unique_id].append(self.onehot_module[(0,0,0)])
else:
self.track_mod_onehot[track_unique_id] = [self.onehot_module[(0,0,0)]]
self.track_coords[track_unique_id] = np.array(self.track_coords[track_unique_id])
from Tkinter import *
from numpy import array
from numpy.random import shuffle
ANCHO = 8
ALTO = 8
MINAS = 10
N = ANCHO * ALTO
# Estos son los colores que tienen los numeros del 0 al 8.
colores = 'white blue green red purple black maroon turquoise gray'.split()
# Crear juego al azar.
campo = array([-1] * MINAS + [0] * (N - MINAS))
shuffle(campo)
campo = campo.reshape((ANCHO, ALTO))
# Contar las minas vecinas en cada celda.
for i in range(ANCHO):
for j in range(ALTO):
if campo[i, j] != -1:
vecindad = campo[max(i - 1, 0):i + 2, max(j - 1, 0):j + 2]
cuenta = (vecindad == -1).sum()
campo[i, j] = cuenta
w = Tk()
juego_terminado = BooleanVar()
juego_terminado.set(False)
faltantes = IntVar()
def prepItems(self, items):
seed(self.seed)
rtn = [o for o in items]
shuffle(rtn)
return rtn
def generate_samples_from_adni2(adni_root,
max_augm_params,
augm_factor,
prefix_name='alz',
test_prc=0.25,
shuffle_data=True,
debug=True):
stage_dirs = {'AD': '/AD/', 'MCI': '/MCI/', 'NC': '/NC/'}
stage_dirs_root = {k: adni_root + v for k, v in stage_dirs.items()}
class_size = {
k: len(os.listdir(stage_dirs_root[k]))
for k in stage_dirs_root
}
print('source patients:', class_size)
ts = int(min(class_size.values()) * test_prc)
test_size = {k: ts for k in stage_dirs_root}
train_size = {k: class_size[k] - test_size[k] for k in stage_dirs_root}
print('source patients used for train & validation:', train_size)
print('source patients used for test', test_size)
train_size_balanced = int(max(train_size.values()) * augm_factor)
test_size_balanced = int(max(test_size.values()) * augm_factor)
print(
'train & validation data will be augmented to %d samples by each class'
% train_size_balanced)
print('test data will be augmented to %d samples by each class' % ts)
sample_sets = {
'train': XSet(name=prefix_name + '_train'),
'test_0': XSet(name=prefix_name + '_test_0'),
'test_1': XSet(name=prefix_name + '_test_1'),
'test_2': XSet(name=prefix_name + '_test_2'),
}
for k in stage_dirs_root:
stage_dir = stage_dirs[k]
patient_dirs = os.listdir(stage_dirs_root[k])
rnd.shuffle(patient_dirs)
test_dirs = patient_dirs[:test_size[k]]
train_dirs = patient_dirs[test_size[k]:]
train_lists = [(k, stage_dir + d + '/SMRI/', stage_dir + d + '/MD/')
for d in train_dirs]
test_lists = [(k, stage_dir + d + '/SMRI/', stage_dir + d + '/MD/')
for d in test_dirs]
sample_sets['train'].add_all(
generate_augm_set(train_lists, train_size_balanced,
max_augm_params))
sample_sets['test_0'].add_all(generate_augm_set(
test_lists, None, None))
sample_sets['test_1'].add_all(
generate_augm_set(test_lists, test_size_balanced,
AugmParams(max_augm_params.shift, sigma=0.0)))
sample_sets['test_2'].add_all(
generate_augm_set(test_lists, test_size_balanced, max_augm_params))
if shuffle_data:
for s in sample_sets:
sample_sets[s].shuffle()
if debug:
for s in sample_sets:
sample_sets[s].print()
return sample_sets
thisInfo = copy.copy(info)
#now add any specific info for this staircase
thisInfo['thisStart'] = thisStart #we might want to keep track of this
thisStair = data.StairHandler(startVal=thisStart,
extraInfo=thisInfo,
nTrials=50,
nUp=1,
nDown=3,
minVal=0.5,
maxVal=8,
stepSizes=[4, 4, 2, 2, 1, 1])
stairs.append(thisStair)
for trialN in range(info['nTrials']):
shuffle(
stairs
) #this shuffles 'in place' (ie stairs itself is changed, nothing returned)
#then loop through our randomised order of staircases for this repeat
for thisStair in stairs:
thisIntensity = thisStair.next()
print 'start=%.2f, current=%.4f' % (thisStair.extraInfo['thisStart'],
thisIntensity)
#---------------------
#run your trial and get an input
#---------------------
keys = event.waitKeys(
) #(we can simulate by pushing left for 'correct')
if 'left' in keys: wasCorrect = True
else: wasCorrect = False
import pickle
from numpy.random import choice, shuffle
candidate_names = ["P + G", "PA + G", "PA + W"]
candidates = [0, 1, 2]
# probabilities = [0.7, 0.2, 0.1]
slide = [0, 0, 0, 0, 1, 1, 2, 2]
choices = []
for i in range(0, 10):
shuffle(slide)
draw = slide
# draw = choice(candidates, 8,
# p=probabilities)
choices.append(draw)
print(i, end="\t")
for j in draw:
print(candidate_names[j].ljust(10) + ' | ', end="\t")
print("")
print("-" * 10 * 13)
pickle.dump(choices, open("choices.pkl", "wb"))
def route_move(state):
"""Swaps two cities in the route."""
a = random.randint(0, len(state) - 1)
b = random.randint(0, len(state) - 1)
state[a], state[b] = state[b], state[a]
def route_energy(state):
"""Calculates the length of the route."""
e = 0
for i in range(len(state)):
e += distance(cities[state[i - 1]], cities[state[i]])
return e
# Start with the cities listed in random order
randState = cities.keys()
random.shuffle(randState)
# Minimize the distance to be traveled by simulated annealing with a
# manually chosen temperature schedule
annealer = Annealer(route_energy, route_move)
state, e = annealer.anneal(randState, 10000000, 0.01,
18000 * len(randState), 9)
while state[0] != 'New York City':
state = state[1:] + state[:1] # rotate NYC to start
print("%i mile route:" % route_energy(state))
for city in state:
print("\t", city)
# Minimize the distance to be traveled by simulated annealing with an
# automatically chosen temperature schedule
autoSchedule = annealer.auto(randState, 4)
print("Started at: " + str(datetime.datetime.now()))
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.INFO)
logger = logging.getLogger()
xs = []
maxlen = 100
max_features = maxlen + 1
from numpy.random import shuffle
r = range(1, maxlen + 1, 1)
for i in range(1000):
shuffle(r)
new_x = r[::]
xs.append(new_x)
def to_one_hot(id):
zeros = [0] * max_features
zeros[id] = 1
return zeros
xs = np.asarray(xs)
ys = map(lambda x: map(to_one_hot, x), xs)
ys = np.asarray(ys)
def shuffle(l):
R.shuffle(l)
return l
from numpy.random import seed from numpy.random import shuffle seed(1) sequence = [i for i in range(20)] print(sequence) shuffle(sequence) print(sequence)
def prepare_epoch(self):
"""
Read random videos of the database
"""
subdirnames = [
'/01/', '/02/', '/03/', '/04/', '/05/', '/06/', '/07/', '/08/',
'/09/'
]
filenames = [
'001.png', '002.png', '003.png', '004.png', '005.png', '006.png',
'007.png', '008.png', '009.png', '010.png', '011.png', '012.png',
'013.png', '014.png', '015.png'
]
burst_nums = np.random.randint(len(subdirnames),
size=len(self.categories * 10))
frame_nums = np.random.randint(self.past_frames,
high=len(filenames) -
self.future_frames,
size=len(self.categories * 10))
i = 0
self.videos = []
self.keys = []
for c in self.categories:
paths = self.video_paths_dict[c]
paths = np.random.permutation(paths)
for p in paths[:3]:
dir_path = p + subdirnames[burst_nums[i]]
if not (os.path.exists(dir_path)):
continue
video = []
for f in range(frame_nums[i] - self.past_frames,
frame_nums[i] + self.future_frames + 1):
img = cv2.imread(dir_path + filenames[f])
img = np.asarray(img, dtype=np.float32)
if self.color:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = np.expand_dims(img, 2)
img = np.asarray(img, dtype=np.float32)
img = img / 255.
video.append(img)
ref_image = video[self.past_frames]
video = np.asarray(video, dtype=np.float32)
video_noised = video + self.sigma * np.random.randn(
video.shape[0], video.shape[1], video.shape[2],
video.shape[3])
video_noised = np.asarray(video_noised, dtype=np.float32)
video_search_gray = rgb_to_gray(video if self.oracle_mode ==
1 else video_noised)
nn = self.ps.compute(video_search_gray, self.past_frames)
self.videos.append((ref_image, video_noised, nn))
ys = range(2 * self.patch_width,
ref_image.shape[0] - 2 * self.patch_width,
self.patch_stride)
xs = range(2 * self.patch_width,
ref_image.shape[1] - 2 * self.patch_width,
self.patch_stride)
xx, yy = np.meshgrid(xs, ys)
xx = np.asarray(xx.flatten(), dtype=np.uint32)
yy = np.asarray(yy.flatten(), dtype=np.uint32)
self.keys.append(
np.stack([i * np.ones([len(xx)], dtype=np.uint32), xx,
yy]).T)
i = i + 1
self.keys = np.concatenate(self.keys, axis=0)
self.num_keys = self.keys.shape[0]
self.indices = [i for i in range(self.num_keys)]
random.shuffle(self.indices)
# Generate a randomized list of angle and direction pairs. Each pair is
# represented as a single integer. The index of the angle (in the angles array)
# is multiplied by 10, and the index of the direction (in the directions array)
# is added to it. Positive values represent horizontal pairs, and negative
# represent vertical.
pairs = list(range(0))
for i in range(trials):
for j in range(len(anglesH)): # Loop through horizontal angles
for k in range(len(directionsH)): # Loop through horizontal directions
pairs.append((j * 10) + k)
if not horizontalOnly:
for l in range(len(anglesV)):
for m in range(len(directionsV)):
pairs.append(-((l * 10) + m))
shuffle(pairs) # Randomize the pairs list
run = 0 # Store the number of trials completed
for pair in pairs: # Loop through the list of pairs
if (pair >= 0): # Horizontal pairs
angle = anglesH[int(pair / 10)] # Angle index = pair/10
dir = directionsH[(pair % 10)] # Direction index = pair%10
else: # Vertical pairs
angle = anglesV[abs(int(pair / 10))] # Angle index = pair/10
dir = directionsV[abs(pair % 10)] # Direction index = pair%10
size = angle / 10 # Set initial letter height
if (size == 0): # Ensure initial letter height is not 0
size = 1
# Initialize trial variables related to staircase algorithm
def main(wordvecfile="sentiment-data/word-vectors-refine.txt",
trainfile="sentiment-data/train.csv",
testfile="sentiment-data/test.csv"):
word_to_index, index_to_word, word_vectors = read_word_vectors(wordvecfile)
word_vectors = np.array(word_vectors, dtype=np.float32)
train_data, train_labels, maxsenlen, minsenlen = read_data(trainfile)
train_labels = np.array(train_labels)
no_train_sentences = len(train_data)
train_data_ints = np.zeros((no_train_sentences, maxsenlen), dtype=np.int32)
print("Maximum sentence length in training data: ", maxsenlen)
print("Minimum sentence length in training data: ", minsenlen)
print("Total no. of sentences in training data : ", no_train_sentences)
# convert each sentence into integer sequence
for i, sentence in enumerate(train_data):
train_data_ints[i, :] = get_sentence_in_word_indices(
train_data[i], word_to_index, maxsenlen)
test_data, test_labels, maxsenlen_test, minsenlen_test = read_data(
testfile)
test_labels = np.array(test_labels)
no_test_sentences = len(test_data)
test_data_ints = np.zeros((no_test_sentences, maxsenlen), dtype=np.int32)
assert (maxsenlen_test <= maxsenlen)
print("Maximum sentence length in testing data: ", maxsenlen_test)
print("Minimum sentence length in testing data: ", minsenlen_test)
print("Total no. of sentences in testing data : ", no_test_sentences)
# convert each test sentence into integer sequence
for i, sentence in enumerate(test_data):
test_data_ints[i, :] = get_sentence_in_word_indices(
test_data[i], word_to_index, maxsenlen)
# RNN Parameters
batch_size = 100
n_tr_batches = np.int(np.ceil(no_train_sentences / batch_size))
# Split the training data into different batches
train_data_indices = np.arange(no_train_sentences)
nr.shuffle(train_data_indices)
train_data_indices = np.array_split(train_data_indices, n_tr_batches)
batched_train_data = [
train_data_ints[indices] for indices in train_data_indices
]
batched_train_labels = [
train_labels[indices] for indices in train_data_indices
]
n_vrnn_cell = 64
n_classes = 2
maxiter = 10
wordvecdim = 50
# reset the default graph
tf.reset_default_graph()
# Create placeholder for labels
t_labels = tf.placeholder(tf.float32, [None, n_classes]) # labels
t_data = tf.placeholder(tf.int32,
[None, maxsenlen]) # training or test data
# Create variables to hold the 3D tensor data of examples, words in sentences, word vectors
indata = tf.nn.embedding_lookup(word_vectors, t_data)
# Setup VRNN
vrnn_cell = tf.nn.rnn_cell.BasicRNNCell(n_vrnn_cell)
outputs, state = tf.nn.dynamic_rnn(vrnn_cell, indata, dtype=tf.float32)
# weights for last softmax
W = tf.Variable(tf.random_uniform([n_vrnn_cell, n_classes]))
b = tf.Variable(tf.constant(0.1, shape=[n_classes]))
H = tf.transpose(outputs, [1, 0, 2])
h_final = tf.gather(H, int(H.get_shape()[0]) - 1)
prediction = tf.matmul(h_final, W) + b
correct_prediction = tf.equal(tf.argmax(prediction, 1),
tf.argmax(t_labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=prediction,
labels=t_labels))
optimizer = tf.train.AdamOptimizer().minimize(loss)
sess = tf.Session()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
for epoch in xrange(maxiter):
for i in xrange(n_tr_batches):
sess.run(optimizer, {
t_data: batched_train_data[i],
t_labels: batched_train_labels[i]
})
if ((epoch + 1) % 2 == 0):
save_path = saver.save(sess,
"models/pretrained_vrnn.ckpt",
global_step=epoch)
print("Saved checkpoint to %s" % save_path)
print(
"Accuracy: ",
sess.run(accuracy,
feed_dict={
t_data: test_data_ints,
t_labels: test_labels
}))