def act():
try:
print("EXECUTING>>>>")
webscraper.webscr(str(svalue.get()))
analyser.analyse()
my_msg.set("Scraping done!")
except:
my_msg.set("Enter valid URL!")
def launch_analyses(source_path, start_date, end_date, symbol, out_root, gen_title):
(dates, high, low, openn, close, volume) = load_columns(source_path, start_date, end_date)
max_data_date = max(dates)
if end_date > max_data_date:
end_date = max_data_date
min_data_date = min(dates)
if start_date < min_data_date:
start_date = min_data_date
print('source data @ {0}'.format(source_path))
print('time sample bounds : {0} - {1}'.format(format_dt(start_date), format_dt(end_date)))
file_name = symbol.replace(' ', '') + '.png'
return analyse(symbol, start_date, end_date, dates, openn, high, low, close, volume, out_root, gen_title)
from connector import fetch
from analyser import Normalizer
from analyser import analyse
from indexor import Index
from searcher import Searcher
proc = Normalizer()
myDocs = analyse(fetch('../20news-bydate'), proc)
myIndex = Index()
myIndex.build(myDocs)
myIndex.saveIndex("index")
mySearcher = Searcher()
mySearcher.index.loadIndex("index")
#words = ["do", "we", "fall", "to", "understand", "love"]
#print mySearcher.search(words)
#words = ["do", "we"]
#print mySearcher.search(words)
data = conn.recv(BUFFER_SIZE)
if not data:
break
f.write(data)
print("writing...")
return filename
#Loop endlessly
while True:
#While filename is false try to recieve filename from host
filename = False
while not filename:
try:
filename = receive()
except:
raise
#Send file to analyser to analyse
analyser.analyse(filename)
#If pe-sieve.json is of 0 then pe-sive failed so return "failed"
if os.stat("pe-sieve.json").st_size == 0:
print("pe-sieve failed.")
with open("pe-sieve.json", "w") as f:
f.write("failed")
#Else return pe-seive results
else:
send("pe-sieve.json")
def load_mnist_dataset(self):
with gzip.open('./mnist.pkl.gz', 'rb') as f:
train_set, valid_set, test_set = pickle.load(f)
train_set = zip(train_set[0], train_set[1])
valid_set = zip(valid_set[0], valid_set[1])
test_set = zip(test_set[0], test_set[1])
return train_set, valid_set, test_set
def show_mnist_grid(self, model, test_set, n=10):
test_data = test_set[0]
fig, ax = plt.subplots(nrows=n, ncols=n, sharex=True, sharey=True)
ax = ax.flatten()
for i in xrange(n**2):
y = model.forward(test_data[i])
output = np.argmax(y)
img = test_data[i].reshape(28, 28)
ax[i].imshow(img, cmap='Greys', interpolation='nearest')
ax[i].set_title('%d' % output)
ax[0].set_xticks([])
ax[0].set_yticks([])
plt.tight_layout()
experiment = MnistExperiment()
results = analyser.analyse(experiment.run,
learning_rate=[0.01],
batch_size=60,
epochs=[5],
train_set_percentage=[1])
analyser.plot_analyser_results(results)
def render_result():
stock = request.form['stock']
scraped_data = scrape(stock)
analysis_result = analyse(scraped_data)
return render_template('results.html', suggestion=analysis_result)
def post_method(): complaint = request.form['data'] output = analyse(complaint) return output