def update_boids(boids, time):
for index in range(len(boids)):
b = boids[index]
b[2] += cohesion(index, boids)[0]*10
b[3] += cohesion(index, boids)[1]*10
b[2] += separate(index, boids)[0]*5
b[3] += separate(index, boids)[1]*5
b[2] += align(index, boids)[0]
b[3] += align(index, boids)[1]
# Limit velocity to 500 pixels per second horizontally and vertically
b[2] = speed_limit(b[2], 500)
b[3] = speed_limit(b[3], 500)
# Update the boid's position based on its velocity and the
# time that has passed since the last update.
b[0] += float(b[2])/1000 * time
b[1] += float(b[3])/1000 * time
# Make the boid bounce off the walls.
if b[0] < 0:
b[0] = 0
b[2] = -b[2]
elif b[0] > WIDTH:
b[0] = WIDTH
b[2] = -b[2]
if b[1] < 0:
b[1] = 0
b[3] = -b[3]
elif b[1] > HEIGHT:
b[1] = HEIGHT
b[3] = -b[3]
def update_boids(boids, time, SEPARATION_MULTIPLIER, COHESION_MULTIPLIER, align_on, sl):
for index in range(len(boids)):
b = boids[index]
b[2] += cohesion(index, boids)[0] * COHESION_MULTIPLIER
b[3] += cohesion(index, boids)[1] * COHESION_MULTIPLIER
b[2] += separate(index, boids, random_color, b[4])[0] * SEPARATION_MULTIPLIER
b[3] += separate(index, boids, random_color, b[4])[1] * SEPARATION_MULTIPLIER
if align_on == "y" or align_on == "Y":
b[2] += align(index, boids)[0]
b[3] += align(index, boids)[1]
b[4] = separate(index, boids, random_color, b[4])[2]
# Limit velocity to 500 pixels per second horizontally and vertically
b[2] = speed_limit(b[2], sl)
b[3] = speed_limit(b[3], sl)
# Update the boid's position based on its velocity and the
# time that has passed since the last update.
b[0] += float(b[2]) / 1000 * time
b[1] += float(b[3]) / 1000 * time
# Make the boid bounce off the walls.
if b[0] < 0:
b[0] = 0
b[2] = -b[2]
elif b[0] > WIDTH:
b[0] = WIDTH
b[2] = -b[2]
if b[1] < 0:
b[1] = 0
b[3] = -b[3]
elif b[1] > HEIGHT:
b[1] = HEIGHT
b[3] = -b[3]
def MonitarTL():
global connAM, dbAM, connPM, dbPM
global Lastid
global oldtweet
global searchKey
tweets = None
tweets = search.searchmain(searchKey, twitter, Lastid, "20")
if (tweets == None):
print("line24")
return None
elif (len(tweets["statuses"]) == 0):
print("line27")
return None
else:
Lastid = tweets["statuses"][0][u'id_str']
for tweet in tweets["statuses"]:
try:
if (key2 in tweet["text"] or key3 in tweet["text"]):
doneTodos = separate.separate(tweet["text"], searchKey,
key2, key3)
for doneTodo in doneTodos:
print("doneTodo: " + doneTodo)
dbAM.execute("DELETE FROM tweets WHERE todo=",
(doneTodo, ))
else:
print("tweet.text: ", tweet["text"])
todos = separate.separate(tweet["text"], searchKey, key2,
key3)
reply.reply(
twitter, tweet[u'id_str'],
separate.delet(tweet["text"], searchKey, key2, key3) +
"\nをタスクに追加だね！\n報告しないと責めるよ！")
now = datetime.datetime.now()
if (now.hour < 12):
db = dbAM
print("AM")
else:
db = dbPM
print("PM")
for todo in todos:
db.execute(
"INSERT INTO tweets (todo, tweetId, userId) VALUES(?, ?, ?)",
(todo, int(tweet["id"]), int(tweet["user"]["id"])))
except Exception as e:
print(e)
connAM.commit()
connPM.commit()
connAM.commit()
connPM.commit()
def arithmetic_arranger(problems, solution=False):
"""
Takes a list of addition or subtraction problems and reformats the
problems vertically for easy reading.
"""
mainDict = {}
# Test problems argument for five or fewer elements.
pcopy = problems[:]
if (len(pcopy) > 5):
return "Error: Too many problems."
# Apply separate() to each problem to break down into parts.
# Log parts into the main Dictionary under numerical keys.
counter = 0
for prob in problems:
aDict = separate(prob)
if type(aDict) == str:
return aDict
mainDict['Problem%s' % str(counter + 1)] = aDict
counter += 1
# Add solutions to problems if optional arg is True.
if solution == True:
mainDict = answer(mainDict)
# Apply format_lines() to each problem key in mainDict.
arranged_problems = format_lines(mainDict, solution)
return arranged_problems
async def main():
if (not os.path.exists('output_sound')):
os.mkdir('output_sound')
yc = subprocess.Popen(
'yc iam create-token', shell=True, stdout=subprocess.PIPE
) #мне было лень получать токен при помощи python, поэтому использую консоль яндекса
yc_token = yc.stdout.read().decode("utf-8")
yc_token = yc_token.split(
'\n'
) #это нужно, чтобы убрать символ новой строки и не включать в переменную информацию, которая может идти после токена
vk_token = os.getenv('VK_TOKEN')
joke = await getJoke(vk_token)
print(joke)
joke_separated = separate(joke)
path = os.getcwd()
tts_setup_file = open('{}/input_sound/tts_setup.ogg'.format(path), 'wb')
tts_punchline_file = open('{}/input_sound/tts_punchline.ogg'.format(path),
'wb')
tts_setup = await getTts(joke_separated[0], yc_token[0])
tts_punchline = await getTts(joke_separated[1], yc_token[0])
tts_setup_file.write(tts_setup)
tts_punchline_file.write(tts_punchline)
await nuzhdify()
def generate_sentences(filename, n):
# separate text and generate the word weights dictionary
sep_text = separate(read_text(filename))
words_dict = get_word_weights(sep_text)
sentences = []
# begin all sentences with a begin tage
init_sentence = ["<begin>"]
while len(sentences) < n:
sentence_words = init_sentence[:]
while True:
# get the last word of the sentence
prev_word = sentence_words[-1]
# get the list of words and weights for that word
words, weights = zip(
*wc.get_weights_from_dict(words_dict[prev_word]))
# make a weighted choice for the new word based on the previous
new_word = wc.weighted_choice(words, weights)
sentence_words.append(new_word)
# if an end tag is found and the sentence list is more than two,
# format the sentence into a string and append it to the list
if "<end>" in new_word:
if len(sentence_words) > 2:
sentences.append(format_sentence(sentence_words))
break
return sentences
def generate_sentences(filename, n):
# separate text and generate the word weights dictionary
sep_text = separate(read_text(filename))
words_dict = get_word_weights(sep_text)
sentences = []
# begin all sentences with a begin tage
init_sentence = ["<begin>"]
while len(sentences) < n:
sentence_words = init_sentence[:]
while True:
# get the last word of the sentence
prev_word = sentence_words[-1]
# get the list of words and weights for that word
words, weights = zip(*wc.get_weights_from_dict(words_dict[prev_word]))
# make a weighted choice for the new word based on the previous
new_word = wc.weighted_choice(words, weights)
sentence_words.append(new_word)
# if an end tag is found and the sentence list is more than two,
# format the sentence into a string and append it to the list
if "<end>" in new_word:
if len(sentence_words) > 2:
sentences.append(format_sentence(sentence_words))
break
return sentences
def PC(L):
coord = L[0] + '.xyz'
main = L[1] # name of main fragment
N = int(L[2]) #no. of atoms in main fragment
scaling = float(L[3])
main_charge = int(L[4])
main_mult = int(L[5])
job = L[6]
root = int(L[7])
n = separate.separate(coord, main, N) # n is no. of water molecules
#replace_by_Q.replace_by_Q('uracil.xyz')
for i in range(n):
charge_water.pointcharge('Water' + str(i + 1) + '.xyz')
for i in range(n):
pc_water.pc_water(i + 1, n)
gen_main_pc.gen_main_pc(main, n)
#for i in range (n):
# gen_water_pc.gen_water_pc(i+1)
# make_input.make_input(main+'.xyz',main+'.pc',main_charge,main_mult)
#for i in range (n):
# make_input.make_input('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'.pc')
#submit.submit(main+'_pc')
#for i in range (n):
# submit.submit('Water'+str(i+1)+'_pc')
# En = energy.energy(main+'_pc.out')
#print En
#replace_by_Q.replace_by_Q(main+'.xyz',main+'_pc.out')#update uracil charges from 1st iter
#for i in range (n):
# gen_water_pc.gen_water_pc(i+1,main,N)#gen. pc files for water using updated uracil charges
#for i in range (n):
# make_input.make_input('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'.pc')
#for i in range (n):
# submit.submit('Water'+str(i+1)+'_pc')
# Eo = 0
# j = 0
#epsilon = 0.000001
#while (abs(Eo-En) >= epsilon):
# j += 1
#print abs(Eo-En)
# print 'Starting iteration no. '+str(j)
# Eo = En
# replace_by_Q.replace_by_Q(main+'.xyz',main+'_pc.out')#update uracil charges
# for i in range(n):
# replace_by_Q.replace_by_Q('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'_pc.out')#update water charges
# for i in range (n):
# pc_water.pc_water(i+1,n)
# gen_main_pc.gen_main_pc(main,n)#gen. uracil pc file
# for i in range (n):
# gen_water_pc.gen_water_pc(i+1,main,N)#gen. water pc file
# make_input.make_input(main+'.xyz',main+'.pc',main_charge,main_mult)#make input files with updated charges
# for i in range (n):
# make_input.make_input('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'.pc')
# submit.submit(main+'_pc')#submit input files
# for i in range (n):
# submit.submit('Water'+str(i+1)+'_pc')
# En = energy.energy(main+'_pc.out')
# print "Energy of "+main+" after iteration "+str(j)+" is "+str(En)
# print "delta E = " + str(abs(Eo-En))
# print 'Converged energy = '+str(En)+'\n\n'
# print 'Scaling the water point charges'
# for i in range(n):
# replace_by_Q.replace_by_Q('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'_pc.out',scaling)
# for i in range (n):
# pc_water.pc_water(i+1,n)
# gen_main_pc.gen_main_pc(main,n)#gen. main fragment pc file
# print 'Submitting EOM file'
# make_DLPNO_input.make_DLPNO_input(main+'.xyz',main+'.pc',job)
# submit.submit(main+'_'+job+'_EOM_pc')
# submit_CHELPG.submit(main+'_'+job+'_EOM_pc.root'+str(root)+'.'+job)
#Regenerating water charges from EE/IP/EA calc. of main frag.
# replace_by_Q_EOM.replace_by_Q_EOM(main+'.xyz',main+'_'+job+'_EOM_pc.root'+str(root)+'.'+job+'.out')
# for i in range (n):
# gen_water_pc.gen_water_pc(i+1,main,N)#gen. pc files for water using updated uracil charges
# for i in range (n):
# make_input.make_input('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'.pc')
# for i in range (n):
# submit.submit('Water'+str(i+1)+'_pc')
# for i in range(n):
# replace_by_Q.replace_by_Q('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'_pc.out')#update water charges
#Now we have final water charges
# gen_main_pc.gen_main_pc(main,n)#gen. main fragment pc file
# print 'Final files generated'
# print 'Submitting '+job+' file'
# make_DLPNO_input.make_DLPNO_input(main+'.xyz',main+'.pc')
# submit.submit(main+'_IP_DLPNO_pc')
# print 'IP values obtained'
make_CIS_input.make_DLPNO_input(main + '.xyz', main + '.pc', job)
submit.submit(main + '_' + job + '_CIS_pc')
print main + '_' + job + '_CIS_pc.out' + ' ' + main + ' ' + str(root)
# append_STATE_CIS.append_IROOT(main+'_'+job+'_EOM_pc.out',main,root)
return
#coding:utf-8 import separate import filer import emo_cls str = '恐惧恐惧恐惧恐惧恐惧恐惧恐惧' str = filer.filter(str) seg = separate.separate(str) print emo_cls.classify(seg)
# Final data variables X and target variables Y
X = np.array(one_hot)
Y = np.array(res)
## Compare accuracy of naive Bayes and logistic regression before finding best learning rate
# All datasets will use for logistic regression the same learning rate = 0.01 and # iterations = 500
rate = 0.01
iterations = 500
log_model = log_regression(rate, iterations)
X = log_model.bias(X) # add bias column
# Separate training and testing sets
X_train, Y_train, X_test, Y_test = separate.separate(X, Y)
## Logistic regression
# train the data
fit_iono = log_model.fit(X_train, Y_train)
# Cross validation
validation = cross_validation(rate, max_iterations=10000)
score = validation.evaluate_log(X_train, Y_train)
print("Averaged training accuracy for Logistic Regression: ", score)
# Test data
pre = log_model.predict(X_test, fit_iono)
acc = log_model.evaluate_acc(pre, Y_test)
print("Accuracy on testing data for Logistic Regression: ", acc)
def XPOL_n(L, iter_no):
coord = L[0] + '.xyz'
main = L[1] # name of main fragment
N = int(L[2]) #no. of atoms in main fragment
scaling = float(L[3])
main_charge = int(L[4])
main_mult = int(L[5])
job = L[6]
root = int(L[7])
f = open(main + '_time.txt', 'w')
f.close()
n = separate.separate(coord, main, N) # n is no. of water molecules
#replace_by_Q.replace_by_Q('uracil.xyz')
for i in range(n):
charge_water.pointcharge('Water' + str(i + 1) + '.xyz')
for i in range(n):
pc_water.pc_water(i + 1, n)
for k in range(iter_no):
gen_main_pc.gen_main_pc(main, n)
make_input.make_input(main + '.xyz', main + '.pc', main_charge,
main_mult)
submit.submit(main + '_pc')
time = comp_time.comp_time(main + '_pc.out') #comp. time calc.
s = 'Time taken for iteration number ' + str(
k + 1) + ' for main fragment :\n'
s += time + '\n\n'
f = open(main + '_time.txt', 'a')
f.write(s)
f.close()
En = energy.energy(main + '_pc.out')
f = open(main + '_energy.txt', 'a')
s = 'Energy after iteration no. ' + str(k +
1) + ' : ' + str(En) + '\n\n'
f.write(s)
f.close()
replace_by_Q.replace_by_Q(
main + '.xyz',
main + '_pc.out') #update uracil charges from 1st iter
for i in range(n):
gen_water_pc.gen_water_pc(
i + 1, main,
N) #gen. pc files for water using updated uracil charges
for i in range(n):
make_input.make_input('Water' + str(i + 1) + '.xyz',
'Water' + str(i + 1) + '.pc')
for i in range(n):
submit.submit('Water' + str(i + 1) + '_pc')
time = comp_time.comp_time('Water' + str(i + 1) +
'_pc.out') #comp. time calc.
s = 'Time taken for iteration number ' + str(
k + 1) + ' for water mol. no. ' + str(i + 1) + ' :\n'
s += time + '\n\n'
f = open(main + '_time.txt', 'a')
f.write(s)
f.close()
for i in range(n):
replace_by_Q.replace_by_Q('Water' + str(i + 1) + '.xyz',
'Water' + str(i + 1) +
'_pc.out') #update water charges
for i in range(n):
pc_water.pc_water(i + 1, n)
gen_main_pc.gen_main_pc(main, n) #gen. uracil pc file
# for i in range (n):
# gen_water_pc.gen_water_pc(i+1,main,N)#gen. water pc file
# make_input.make_input(main+'.xyz',main+'.pc',main_charge,main_mult)#make input files with updated charges
# for i in range (n):
# make_input.make_input('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'.pc')
# submit.submit(main+'_pc')#submit input files
# for i in range (n):
# submit.submit('Water'+str(i+1)+'_pc')
# En = energy.energy(main+'_pc.out')
# print "Energy of "+main+" after iteration "+str(j)+" is "+str(En)
# print "delta E = " + str(abs(Eo-En))
## print 'Converged energy = '+str(En)+'\n\n'
## print 'Scaling the water point charges'
## for i in range(n):
## replace_by_Q.replace_by_Q('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'_pc.out',scaling)
## for i in range (n):
## pc_water.pc_water(i+1,n)
## gen_main_pc.gen_main_pc(main,n)#gen. main fragment pc file
#############
# print 'Submitting EOM file'
# make_CIS_input.make_DLPNO_input(main+'.xyz',main+'.pc',job)
# submit.submit(main+'_'+job+'_CIS_pc')
#############
# submit_CHELPG.submit(main+'_'+job+'_CIS_pc-iroot'+str(root)+'.csp')
#Regenerating water charges from EE/IP/EA calc. of main frag.
# replace_by_Q_EOM.replace_by_Q_EOM(main+'.xyz',main+'_'+job+'_CIS_pc.root'+str(root)+'.'+job+'.out')
# for i in range (n):
# gen_water_pc.gen_water_pc(i+1,main,N)#gen. pc files for water using updated uracil charges
# for i in range (n):
# make_input.make_input('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'.pc')
# for i in range (n):
# submit.submit('Water'+str(i+1)+'_pc')
# for i in range(n):
# replace_by_Q.replace_by_Q('Water'+str(i+1)+'.xyz','Water'+str(i+1)+'_pc.out')#update water charges
#Now we have final water charges
# gen_main_pc.gen_main_pc(main,n)#gen. main fragment pc file
# print 'Final files generated'
# print 'Submitting '+job+' file'
# make_DLPNO_input.make_DLPNO_input(main+'.xyz',main+'.pc')
# submit.submit(main+'_IP_DLPNO_pc')
# print 'IP values obtained'
# make_CIS_input.make_DLPNO_input(main+'.xyz',main+'.pc',job)
# submit.submit(main+'_'+job+'_CIS_pc')
# append_IROOT.append_IROOT(main+'_'+job+'_EOM_pc.out',main,root)
return En
## Cross validation
#score = bayes_model.cross_validation(X_train,Y_train, 5)
#print(score)
## Compare accuracy of naive Bayes and logistic regression
# All datasets will use for logistic regression the same learning rate = 0.01 and # iterations = 500
rate = 0.01
iterations = 500
log_model = log_regression(rate, iterations)
X = log_model.bias(X) # add bias column
# Separate training and testing sets
X_train, Y_train, X_test, Y_test = separate.separate(X, Y)
## Logistic regression
# train the data
fit_iono = log_model.fit(X_train, Y_train)
# Cross validation
validation = cross_validation(rate, max_iterations=500)
score = validation.evaluate_log(X_train, Y_train)
print("Averaged training accuracy for Logistic Regression: ", score)
# Test data
pre = log_model.predict(X_test, fit_iono)
acc = log_model.evaluate_acc(pre, Y_test)
print("Accuracy on testing data for Logistic Regression: ", acc)
import charge_water
import pc_water
import sys, os
import subprocess
import shutil
import make_input
import replace_by_Q
import gen_water_pc
import gen_uracil_pc
import charge_water
import submit
import energy
N = input("Enter the coordinates file: ")
n = separate.separate(N)
#replace_by_Q.replace_by_Q('uracil.xyz')
for i in range(n):
charge_water.pointcharge('Water' + str(i + 1) + '.xyz')
for i in range(n):
pc_water.pc_water(i + 1, n)
gen_uracil_pc.gen_uracil_pc(n)
#for i in range (n):
# gen_water_pc.gen_water_pc(i+1)
make_input.make_input('uracil.xyz', 'uracil.pc')
outF = np.fft.rfft(out)
outF[-1] = 0
out = np.fft.irfft(outF)
return out
rate, aWav = wavfile.read("a.wav")
rate, bWav = wavfile.read("b.wav")
n = len(aWav)
m = len(bWav)
a = aWav
b = bWav
pine, (fir, ) = separate(a, b)
wavfile.write("pine.wav", rate, np.tile(normalize(pine), 20))
wavfile.write("fir.wav", rate, np.tile(normalize(fir), 20))
aHat, bHat = combineIngredients(pine, fir, m)
wavfile.write(
"aHat.wav", rate,
np.concatenate((np.tile(normalize(a), 20), np.tile(normalize(aHat), 20))))
wavfile.write(
"bHat.wav", rate,
np.concatenate((np.tile(normalize(b), 20), np.tile(normalize(bHat), 20))))
hats = [a, b]
for i in xrange(20):
aHat, cHat = combineIngredients(pine, fir, m**(i + 2) // n**(i + 1))
hats.append(cHat)
