def __init__(self):
Frame.__init__(self)
self.master.title("ASL Game")
self.master.minsize(width=500,height=500)
self.grid()
self.__dataPane = Frame(self)
self.__dataPane.grid(row=0, column=0)
self.__mode1Frame = Frame(self)
self.__mode2Frame = Frame(self)
## self.__mode3Frame = Frame(self)
self.__dp = DataProcessing()
self.__whole = self.__dp.getWholeSignDict()
self.__NumLtr = self.__dp.getNumLtrDict()
self.__homeButton = Button(self.__dataPane, text="Home",\
command=self.__home)
self.__welcome = Label(self.__dataPane, text="Choose a mode")
self.__mode1Button = Button(self.__dataPane, text="Mode 1",\
command=self.__mode1)
self.__mode2Button = Button(self.__dataPane, text="Mode 2",\
command=self.__mode2)
## self.__mode3Button = Button(self.__dataPane, text="Mode 3",\
## command=self.__mode3)
self.__homeButton.grid(row=1, column=1)
self.__welcome.grid(row=2, column=2)
self.__mode1Button.grid(row=3, column=1)
self.__mode2Button.grid(row=3, column=2)
#self.__mode3Button.grid(row=3, column=3)
mainloop()
def SaveDataToFile():
try:
objF = DataProcessing.DataProcessing()
objF.FileName = "CustomerData.txt"
objF.TextData = Customers.CustomerList.ToString()
objF.SaveData()
except Exception as e:
print(e)
def Operate(Specs):
dataObj = DataProcessing.DataProcessing(Specs)
# TBD make sure that input data is valid etc.
# TBD back test results against real data
# TBD Error handling
# TBD fix model paths
#print("----------------------------------------------------------")
#print("event forecast started.")
#print(datetime.now())
#print("----------------------------------------------------------")
if not hasattr(dataObj, "data"):
print("no valid data could be extracted.")
return
if Specs.Mode == "2":
print("No Neural Network support at the moment.")
return
if not int(Specs.AnzahlPrognosen) == 1 or not Specs.AnzahlPrognosen:
print(
"corrected wrong configuration: Only realtime forecast is considered"
)
Specs.AnzahlPrognosen = 1
print("input data: ", dataObj.datapath)
#load model
try:
mdlObj = generateModel(Specs, dataObj)
except Exception as e:
print("problems while loading model: " + e)
try:
ForecastDyn = mdlObj.predictOperative(Specs.horizont)
timeseriesNF = dataObj.data["NoFilter"]
except Exception as e:
print("problems while prediction:\n" + e)
try:
print("creating csv.")
createCsv(ForecastDyn, Specs)
print("done")
except Exception as e:
print("no csv could be created:\n" + e)
try:
DataDyn = timeseriesNF[-Specs.horizont:]
multi = "\n##### " + str(
Specs.horizont) + "-step Prediction against last values ##### \n"
print(multi)
sys.stdout.flush()
FDyn = ErrorAnalysis.ErrorAnalysis(DataDyn, ForecastDyn)
print(FDyn.criterias)
sys.stdout.flush()
except Exception as e:
print(e)
def SaveDataToFile():
try:
objF = DataProcessing.DataProcessing()
objF.FileName = "EmployeeData.txt"
objF.TextData = Employees.EmployeeList.ToString()
print("Reached here")
objF.SaveData()
except Exception as e:
print(e)
import os import re # import pandas as pd # import sys import null import numpy as np from requests.packages import chardet # reload(sys) import DataPreprocessing as dp import CreateDictionary as cd import DataProcessing as dproc a1=dp.DataPreprocessing() a2 = cd.CreateDictionary() a3 = dproc.DataProcessing() # f='D:\scientific work\InformationExtracting\processeddataex\qword-20151019144240604.xml' # # # print a.find_first_occurrences_of_header(f,u'ТАЛОН НА ОКАЗАНИЕ ВМП ИЗ ПАК') # # # print a.find_last_occurrence_of_header(f,u'ТАЛОН НА ОКАЗАНИЕ ВМП ИЗ ПАК') # text = a1.find_all_occurrences_of_header(f,u'ЭПИДАНАМНЕЗ') # # # print text # sent = a2.sentences_list(text) # colloc = a2.collocations_list(sent) # words = a2.words_list(colloc) # # print sent # # print colloc # frequency lists for stationary diagnosis path = "D:\scientific work\data\All_Without_Tags" # path = "D:\scientific work\data\All_Part_WithoutTags"
def BackTesting(Specs):
#warning: Format YYYY-MM-DD (~) > 1000x faster than DD.MM.YYYY
dataObj = DataProcessing.DataProcessing(Specs)
modelObj = generateModel(Specs, dataObj)
timeseriesNF = dataObj.data["NoFilter"]
print("\nprediction with generated model" + str(Specs.order) + "x" +
str(Specs.sorder) + "\n\n")
if (Specs.Mode == "1"):
##### -1-Step Prediction- #####
pred1 = modelObj.predict1Step()
F1 = ErrorAnalysis.ErrorAnalysis(timeseriesNF,
modelObj.predicted.predicted_mean)
one = "##### 1-step Prediction ##### \n"
print(one)
sys.stdout.flush()
print(F1.criterias)
sys.stdout.flush()
##### -Multi-Step Prediction- #####
ForecastDyn = modelObj.predictDyn(nstep=Specs.horizont,
n=Specs.sorder[0],
delay=Specs.delay,
anzahl=Specs.AnzahlPrognosen)
DataDyn = timeseriesNF.loc[ForecastDyn.index]
FDyn = ErrorAnalysis.ErrorAnalysis(DataDyn, ForecastDyn)
multi = "\n##### " + str(Specs.horizont) + "-step Prediction ##### \n"
print(multi)
sys.stdout.flush()
print(FDyn.criterias)
sys.stdout.flush()
try:
import matplotlib.pyplot as plt
ax = plt.subplot(2, 1, 1)
dataObj.visualize(ax, '1-step Forecast', timeseriesNF,
pred1.predicted_mean)
ax1 = plt.subplot(2, 1, 2)
dataObj.visualize(ax1,
str(Specs.horizont) + '-step Forecast', DataDyn,
ForecastDyn)
plt.show()
except Exception as e:
print(e)
print("No visualization possible.")
else:
print("\npredictions with generated Neural Network")
trainPred, trainOut, testPred, testOut = modelObj.predict()
Ftrain = ErrorAnalysis.ErrorAnalysis(trainOut.flatten(),
trainPred.flatten())
Ftest = ErrorAnalysis.ErrorAnalysis(testOut.flatten(),
testPred.flatten())
try:
import matplotlib.pyplot as plt
ax = plt.subplot(2, 1, 1)
dataObj.visualize(ax,
str(Specs.horizont) + '-step Forecast',
trainOut.flatten(), trainPred.flatten())
ax1 = plt.subplot(2, 1, 2)
dataObj.visualize(ax,
str(Specs.horizont) + '-step Forecast',
testOut.flatten(), testPred.flatten())
except:
print("No visualization possible.")
print("Training:\n")
print(Ftrain.criterias)
sys.stdout.flush()
print("\nTest:\n")
print(Ftest.criterias)
sys.stdout.flush()
plt.show()
# Protection
if len(sys.argv) < 2:
print("1 argument needed: train_dataset")
exit(1)
# Option EarlyStopping
delta_min_loss = 0.00001
for i, arg in enumerate(sys.argv):
if "-earlystopping" in arg.lower() and i + 1 < len(sys.argv):
delta_min_loss = sys.argv[i + 1]
# Parsing
train_dataset, targets = parse(sys.argv[1])
dataProcessing = DataProcessing(train_dataset, targets, columns=columns_name)
dataProcessing.normalize()
train_dataset, targets = dataProcessing.get_data(
data_type="2d_array",
# shuffle=False,
shuffle=True,
)
if len(train_dataset) != len(targets):
print(f"len(train_dataset) = {len(train_dataset)}")
print(f"len(targets) = {len(targets)}")
exit(0)
# Create model with random weights
models = [Logreg(len(columns_name), name=name) for name in house_matrix]
model = [[float(x) if x else 0 for x in neuron.split(',')]
for neuron in model[:-1].split("\n")]
return features, model
# Protection
if len(sys.argv) != 3:
print("2 arguments needed: dataset weights")
exit(1)
# Parsing
test_dataset, model = parse(sys.argv[1], sys.argv[2])
dataProcessing = DataProcessing(test_dataset, columns=columns_name)
dataProcessing.normalize()
test_dataset = dataProcessing.get_data(data_type="2d_array")
print(f"features ({len(test_dataset)} features) : {test_dataset}")
print(f"Weights: {model}")
# Create model with weights already trainned
model = [Logreg(len(weights), weights=weights) for weights in model]
# Create answers file "houses"
houses_file = open("houses.csv", 'w')
houses_file.write("Index,Hogwarts House\n")
for i, features in enumerate(test_dataset):
# !/usr/local/bin/python3
import Persons
person = Persons.Person()
person.FirstName = "James"
person.LastName = "Cameron"
print(person)
print(person.GetObjectCount())
import Customers
customer = Customers.Customer()
customer.Id = 1
customer.FirstName = "Alisha"
customer.LastName = "Banner"
print("This is the Customer Object: ", customer)
import DataProcessing
file = DataProcessing.DataProcessing()
file.FileName = "Testfile.txt"
file.TextData = "Blah"
file.SaveData()
