def mutation(self,probability):
#randomly swap two tuples from sequence
r = random.random()
if r > probability:
idx = range(len(self.perm))
i1, i2 = random.sample(idx, 2)
self.perm[i1], self.perm[i2] = self.perm[i2], self.perm[i1]
def n_images_from_dir(path, num):
file_images = {}
for f in os.listdir(path):
if os.path.isdir(path + f):
for r, d, fil in os.walk(path + f):
idxs = random.sample(range(0, len(fil)), num)
random_imgs = []
for idx in idxs:
random_imgs.append(fil[idx])
file_images[f] = random_imgs
return file_images
def rand_n(L, config):
csplit = config.split('_')
ns = csplit[0]
nssplit = ns.split('-')
n = int(nssplit[0])
if len(nssplit) > 1:
s = int(nssplit[1]) * L * n
else:
s = None
random.seed(s)
fock_config = '-'.join([str(i) for i in random.sample(range(L), n)])
if len(csplit) > 1:
config = csplit[1:]
fock_config = ''.join([fock_config, config])
return fock(L, fock_config)
def orderedCrossover(self, ind2):
"""Executes an ordered crossover (OX) on the input
individuals. The two individuals are modified in place. This crossover
expects :term:`sequence` individuals of indices, the result for any other
type of individuals is unpredictable.
:param self: The first individual participating in the crossover.
:param ind2: The second individual participating in the crossover.
:returns: A tuple of two individuals.
Moreover, this crossover generates holes in the input
individuals. A hole is created when an attribute of an individual is
between the two crossover points of the other individual. Then it rotates
the element so that all holes are between the crossover points and fills
them with the removed elements in order.
"""
size = len(self.perm)
a, b = random.sample(range(size), 2)
if a > b:
a, b = b, a
newPerm1 = [0] * size
newPerm2 = [0] * size
for i in range(a,b+1):
newPerm1[i]=self.perm[i] #cut part from 1st parent
newPerm2[i]=ind2.perm[i] #cut part from 2nd parent
i=(b+1)%size
while 0 in newPerm1:
oldi=i
while (ind2.perm[i] in newPerm1):
i = (i + 1) % size
newPerm1[oldi]=ind2.perm[i]
i=(oldi+1)%size
i = (b + 1)%size
while 0 in newPerm2:
oldi = i
while (self.perm[i] in newPerm2):
i = (i + 1) % size
newPerm2[oldi] = self.perm[i]
i = (oldi + 1) % size
return Individual(size,newPerm1),Individual(size,newPerm2)
def str2mat(instr, limit=5, placeholder=None):
'''
Convert string to a vector base on average vector of the composing words.
instr: the inpput string
placeholder: for the non-vocabularies
'''
# make a place-holder: mean of three strange words
mystr = trim_string(instr)
# number of words
L = min(len(mystr), limit)
## padding up
sheet = np.ones((300, limit)) * 2
for l in range(L):
if (mystr[l] in vocab):
sheet[:, l] = c_dict[mystr[l]]
else:
ph = np.ones(300) * 1
ph[random.sample(list(np.arange(300)), 20)] = -3
sheet[:, l] = ph
return L, sheet
def main():
from recommend.data.datasets import load_100k
import random
# 数据集处理
df = load_100k(type='pd').alldata
nums_train = 10
df = df.groupby('user_id').filter(lambda x: len(x) >= nums_train + 10)
data_train = []
data_test = []
for gb, dtfm in df.groupby('user_id'):
dtfm = dtfm.reset_index(drop=True) # 去除原索引
index_sample = random.sample(range(len(dtfm)), nums_train)
data_train.append(dtfm.iloc[index_sample, :])
data_test.append(dtfm.drop(index_sample))
data_train = pd.concat(data_train)
data_test = pd.concat(data_test)
x_train = np.array(data_train[['user_id', 'item_id', 'timestamp']])
y_train = np.array(data_train['rating'])
x_test = np.array(data_test[['user_id', 'item_id', 'timestamp']])
y_test = np.array(data_test['rating'])
lrmf = ListRankMF(0.01, 10, 0.01, 800)
lrmf.fit(x_train, y_train, evals=(x_test, y_test), top=10)
Mouse2.mouseClock = core.Clock() #initialize variables for loops and array manipulation and keeping track of users incorrect inputs wrongCounter=0 looper=0 # Create some handy timers globalClock = core.Clock() # to track the time since experiment started routineTimer = core.CountdownTimer() # to track time remaining of each (non-slip) routine #used as index for arrays and matrixes while keeping track of how many stimuli have been displayed for training and testing looper=0 #generate random order for each run through of training random.seed() order_list=random.sample(range(0, n_stims), n_stims) #resize matrix new_col = data_matrix.sum(1)[...,None] new_col.shape all_data = np.append(data_matrix, new_col, 1) all_data.shape data_matrix=all_data #resize matrix new_col = data_matrix.sum(1)[...,None] new_col.shape all_data = np.append(data_matrix, new_col, 1) all_data.shape data_matrix=all_data #resize matrix #new_col = data_matrix.sum(1)[...,None]
#for i in range(len(knownA)):
# if i % 2 == 0:
# acrL[i]=knownA[i]
# else:
# acrL[i]=unknownA[i]
######
#acrL=range(len(knownA))
####Create a Random List of Acronyms/ ####Change Size based on what you need
#acrLknown = random.sample(knownA,(lSize/2))
acrLknown = [
knownA[i]
for i in sorted(random.sample(xrange(len(knownA)), int(lSize / 2)))
]
acrlunknown = [
unknownA[i]
for i in sorted(random.sample(xrange(len(unknownA)), int(lSize / 2)))
]
#indices = random.sample(range(len(knownA)), int(lSize/2))
#[knownA[i] for i in sorted(indices)]
#print knownA
acrL = acrLknown.extend(acrlunknown)
#acrL=acrLknown
#print acrL
height=0.1,
wrapWidth=None,
ori=0,
color='white',
colorSpace='rgb',
opacity=1,
depth=0.0)
# Initialize components for Routine "trial"
trialClock = core.Clock()
import random
index = 0
sent_ok = 0
count_sent = 0
order = random.sample(range(2, 7), 5)
recall = False
end_order = 0
time_band = False
text = visual.TextStim(win=win,
name='text',
text='default text',
font='Arial',
pos=(0, 0),
height=0.1,
wrapWidth=None,
ori=0,
color='black',
colorSpace='rgb',
opacity=1,
depth=-2.0)
cols = 3 + (files * 3) + (final_files * (2 * nReps))
currentCol = cols
data_matrix = import_data('final_test_', final_files - 1, rows, cols)
looper = 0
#make lists of stims used and last testing error
while looper < rows:
stim_list.append(data_matrix[looper][0])
error_list.append(data_matrix[looper][cols - 1])
looper = looper + 1
looper = 0
#choose 8 stims at random (for now later will calculate from errors)
use_list = []
#randomly choose
use_these = random.sample(range(0, rows - 1), (int((rows - 2) / 2)))
#add the random 8 to a new list
for num in use_these:
use_list.append(stim_list[num])
#choose timings for each iti
iti_timings = np.zeros((32, 4))
# matrix that hold parameters for how experiment should run, first created and then shuffled to make pseudo random
# col 1 - iti reps 5-8, col 2 - 0 word, 1 pseudo word, col 3 - 0 reexpose, 1 do nothing
# col 4- -1 do nothing, 0-7 stim to reexpose, col 5- 0 left arrow, 1 right arrow
#8 of each iti timing
#within each 8 1/2 are word and 1/2 are pseudoword
#first word and pseudo word of each 8 is a reexpose so 8 reexposed throughout
x = 0
mStimuli = {} #dictionary of all manmade stimuli - 272
nStimuli = {} #dictionary of all natural stimuli - 272
for i in range(272): #loops through 272 to initialize all stimuli elements
num = i + 1
man = ['M' + str(num) + '.jpg', expInfo['ManmadeKeys'], 'manmade']
mStimuli[i] = man
nat = ['N' + str(num) + '.jpg', expInfo['NaturalKeys'], 'natural']
nStimuli[i] = nat
manSelectedStimuli = [] #list of the randomly selected manmade stimuli - 196
natSelectedStimuli = [] #list of the randomly selected natural stimuli - 196
imgRandShuffle = random.sample(range(len(mStimuli)), 196) #randomly access 196 numbers from 272 to use in the loop to select the 196 stimuli
imgNotSelected = [i for i in range(272)]
for selected in imgRandShuffle:
manSelectedStimuli.append(mStimuli[selected])
natSelectedStimuli.append(nStimuli[selected])
imgNotSelected.remove(selected)
shuffle(imgNotSelected)
varSaveFile = "blockData" + expInfo['participant'] + ".txt"
varSaver = open(os.path.join(newpath, varSaveFile), 'w')
varSaver.write("ManmadeKeys:" + str(expInfo['ManmadeKeys']))
varSaver.write("\nNaturalKeys:" + str(expInfo['NaturalKeys']))
varSaver.write("\nimgRandShuffle:" + str(imgRandShuffle))
varSaver.write("\nPracticeSet:" + str(imgNotSelected[0:10]))
#Create the List of Acronyms with known and Unknown #for i in range(len(knownA)): # if i % 2 == 0: # acrL[i]=knownA[i] # else: # acrL[i]=unknownA[i] ###### #acrL=range(len(knownA)) ####Create a Random List of Acronyms/ ####Change Size based on what you need #acrLknown = random.sample(knownA,(lSize/2)) acrLknown = [knownA[i] for i in sorted(random.sample(xrange(len(knownA)), int(lSize/2))) ] acrlunknown = [unknownA[i] for i in sorted(random.sample(xrange(len(unknownA)), int(lSize/2))) ] #indices = random.sample(range(len(knownA)), int(lSize/2)) #[knownA[i] for i in sorted(indices)] #print knownA acrLknown.extend(acrlunknown) acrL=acrLknown print acrL random.shuffle(acrL) #if lSize is not 0: # acrL=acrL[:lSize]
balloonValMsg.setText(u"This balloon value:\n$%.2f" %
thisBalloonEarnings,
log=False)
# *bankedMsg* updates
if t >= 0.0 and bankedMsg.status == NOT_STARTED:
# keep track of start time/frame for later
bankedMsg.tStart = t # underestimates by a little under one frame
bankedMsg.frameNStart = frameN # exact frame index
bankedMsg.setAutoDraw(True)
if bankedMsg.status == STARTED: # only update if being drawn
bankedMsg.setText(u"You have banked:\n$%.2f" % bankedEarnings,
log=False)
if event.getKeys(['space']): # When a person presses space...
overall = overall + 1
PopIndicator = random.sample(
Samp, 1)[0] # This randomly samples 1 data point.
nPumps = nPumps + 1
if PopIndicator == 0: # If that data point is 0
popped = True # The balloon will pop
continueRoutine = False # The routine will end
elif nPumps > 0: # If it's greater than 0...
Samp = Samp[:
-1] # Make this list shorter by the last item in the list.
# *bankButton* updates
if t >= 0.0 and bankButton.status == NOT_STARTED:
# keep track of start time/frame for later
bankButton.tStart = t # underestimates by a little under one frame
bankButton.frameNStart = frameN # exact frame index
bankButton.status = STARTED
# keyboard checking is just starting