def svm_predict(days=10, offset=0, name='YHOO'):
N = 30
X = np.arange(N).reshape(N, 1)
y = fetchdata.get_data(name).ravel()
Z = np.arange(N + offset, N + days + offset).reshape(days, 1)
svr_lin = SVR(kernel='linear', C=1e3)
y_lin = svr_lin.fit(X, y).predict(Z)
return list(y_lin)
def bayes_predict(name='YHOO'):
# #read data from db
global Y
Y = fetchdata.get_data(name)
print 'The trend is :'
print Y
predicted_price = getm(N + 1)[0][0]
return predicted_price
def mlp_predict(days, offset, name='YHOO'):
N = 30
# Generate sample data
X = np.arange(N).reshape(N, 1)
y = fetchdata.get_data(name).ravel()
Z = np.arange(N + offset, N + days + offset).reshape(days, 1)
# Fit regression model
mlp_adam = MLPRegressor(algorithm='l-bfgs', activation='logistic')
y_log = mlp_adam.fit(X, y).predict(Z)
return y_log
def svm_predict(days=10, offset=0, name='YHOO'):
N = 30
# Generate sample data
X = np.arange(N).reshape(N, 1)
y = fetchdata.get_data(name).ravel()
Z = np.arange(N + offset, N + days + offset).reshape(days, 1)
# Fit regression model
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
# svr_lin = SVR(kernel='linear', C=1e3)
# svr_poly = SVR(kernel='poly', C=1e3, degree=2)
y_rbf = svr_rbf.fit(X, y).predict(Z)
# y_lin = svr_lin.fit(X, y).predict(Z)
# y_poly = svr_poly.fit(X, y).predict(Z)
# print y_rbf
# print y_lin
# print y_poly
return list(y_rbf)
def reply():
num = request.form.get("From")
num = num.replace("whatsapp:", "")
print(num)
msg_text = request.form.get("Body")
if "," in msg_text:
pin = msg_text.split(",")[0]
date = msg_text.split(",")[1]
x = collection.find_one({"NUMBER": num})
try:
status = x["status"]
except:
pass
if (bool(x) == False):
collection.insert_one({"NUMBER": num, "status": "first"})
msg = MessagingResponse()
resp = msg.message(
"""Hello this is T2 from total technology,developed by roni , to get covid vaccine availability related informaion please follow the below
enter your pincode and date separated by comma , for example if your pincode is 1100045 and date you want for 15 th may 2021 , then your input should be
1100045,15-05-2021""")
return (str(msg))
else:
if (status == "first"):
data = get_data(pin, date)
msg = MessagingResponse()
if (data == "invalid pincode"):
resp = msg.message("please entre valid pincode")
return (str(msg))
elif (data == "no centre"):
resp = msg.message(
"no centre found for your given input ,please try again later or else try with find with nearest pincode"
)
return (str(msg))
else:
if (len(data) < 15):
parse_data = json.dumps(data)
parse_data = parse_data.replace("{", "")
parse_data = parse_data.replace("}", "\n\n")
parse_data = parse_data.replace("[", "")
parse_data = parse_data.replace("]", "")
parse_data = parse_data.replace(",", "\n")
resp = msg.message(parse_data)
#print(parse_data)
return (str(msg))
else:
print("abc")
resp1 = msg.message(
"please fid the pdf for more information")
gen_pdf(num, data)
resp1.media(
"http://48261c041578.ngrok.io/Users/roni/eclipse-workspace/VACCINE_PROJECT/"
+ num + ".pdf")
return (str(msg))
else:
msg = MessagingResponse()
resp = msg.message(
"""invalid input , to get covid vaccine availability related informaion please follow the below
enter your pincode and date separated by comma , for example if your pincode is 1100045 and date you want for 15 th may 2021 , then your input should be
1100045,15-05-2021""")
return (str(msg))
print(num)
return bestLabel
def getPredictions(summaries, testSet):
predictions = []
for i in range(len(testSet)):
result = predict(summaries, testSet[i])
predictions.append(result)
return predictions
def getAccuracy(testSet, predictions):
correct = 0
for i in range(len(testSet)):
if testSet[i][-1] == predictions[i]:
correct += 1
return (correct/float(len(testSet))) * 100.0
if __name__ == "__main__":
_data = fetchdata.get_data('project3_dataset2.txt')
# _data = fetchdata.get_data('test.txt')
train_data, test_data = seperate_data(_data)
print len(train_data), len(test_data)
seperated = seperateByClass(train_data)
summary = summarizeByClass(seperated)
# for key in summary:
# print summary[key]
predictions = getPredictions(summary, test_data)
accuracy = getAccuracy(test_data, predictions)
print('Accuracy: {0}%').format(accuracy)