def open_file():
"""select input file and show its data"""
file_name, file_type = QFileDialog.getOpenFileName(MainWindow, '选择文件',
default_path,
'txt(*.txt)')
if file_name == '':
return
temp_input = input.read_file(file_name)
try:
if temp_input.measurement_strategy == '0':
ui.measurement_strategy.setCurrentIndex(0)
ui.total_length.setText(temp_input.len_total)
ui.length_step.setText(temp_input.len_step)
elif temp_input.measurement_strategy == '1':
ui.measurement_strategy.setCurrentIndex(1)
ui.num_of_mea.setText(temp_input.num_of_mea)
ui.frequency.setText(temp_input.frequency)
ui.time_step.setText(temp_input.time_step)
ui.na_average_facotr.setValue(int(temp_input.na_average_factor))
ui.multi_measure.setValue(int(temp_input.multi_measure))
ui.save_directory.setText(temp_input.directory)
input_parameters.directory = temp_input.directory
if temp_input.access_sensor_times == '0':
ui.typein_t.setChecked(True)
input_parameters.access_sensor_times = 0
ui.temperature.setText(temp_input.temperature)
ui.humidity.setText(temp_input.humidity)
elif temp_input.access_sensor_times == '1':
ui.measure_t_once.setChecked(True)
input_parameters.access_sensor_times = 1
elif temp_input.access_sensor_times == '2':
ui.measure_t_repeatedly.setChecked(True)
input_parameters.access_sensor_times = 2
if temp_input.na_state is not None:
ui.NA_state.setText(temp_input.na_state)
input_parameters.motor_comp = temp_input.motor_comp
input_parameters.sensor_comp = temp_input.sensor_comp
input_parameters.NA_identifier = temp_input.NA_identifier
except Exception:
missing_parameters('文件格式错误，请补充相应数据')
from database import DatabaseArchiver
from database import HTTPArchiver
from algorithm import power_management
from input import read_file
from save_output_txt import write_to_txt
from input_charts import input_charts
from config import read_config
import sys
content = sys.stdin.readlines()
data = read_file(content)
output = power_management(data)
config = read_config()
if config[6] == 0:
archiver = DatabaseArchiver('/tmp/BTS.db')
else:
archiver = HTTPArchiver('http://localhost:5000')
for item in data:
archiver.save_measurement(item)
for item in output:
archiver.save_response(item)
archiver.flush()
write_to_txt(output)
input_charts(data)
import matplotlib.ticker as ticker
import numpy as np
from consts import *
from lang import Language
from input import read_file, read_cache
from model import Encoder, Decoder
from training import *
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if USE_CACHE:
input_lang, output_lang, pairs = (read_cache(c) for c in CACHE)
else:
input_lang, output_lang, pairs = read_file(FILE,
INCLUDE_PHRASES,
suffix='i')
random.shuffle(pairs)
train_set = pairs[:int(0.8 * len(pairs))]
test_set = pairs[int(0.8 * len(pairs)):]
validation_set = train_set[int(0.8 * len(train_set)):]
train_set = train_set[:int(0.8 * len(train_set))]
print("{} train {} validation {} test".format(len(train_set),
len(validation_set),
len(test_set)),
flush=True)
# coding UTF-8
from input import read_file, conv_str_to_kana, conv_kana_to_vec, conv_vec_to_kana, calc_accuracy, fix_data
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import LeaveOneOut
import numpy as np
import csv
import pickle
# ファイルから読み込み配列に代入
data = read_file('dataset_for.csv')
# タイトル群をカタカナに変換し,さらに母音子音情報に変換
kana_title = conv_str_to_kana(data[0])
kana_ans = conv_str_to_kana(data[1])
vec_title = conv_kana_to_vec(kana_title, 1, "T")
vec_ans = conv_kana_to_vec(kana_ans, 1, "R")
# 交差検証を実行
loo = LeaveOneOut()
lr = LinearRegression()
vec_ans = np.array(vec_ans)
vec_title = np.array(vec_title)
result = []
result_T = []
count = 0
for train_index, test_index in loo.split(vec_title):
X_train, X_test = vec_title[train_index], vec_title[test_index]
Y_train, Y_test = vec_ans[train_index], vec_ans[test_index]
lr.fit(X_train, Y_train)
Y_pred = lr.predict(X_test)
Y_pred = Y_pred.tolist()
Y_test = Y_test.tolist()
# coding UTF-8
from input import read_file, conv_str_to_kana, conv_kana_to_vec, conv_vec_to_kana
from sklearn import svm
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error
import matplotlib.pyplot as plt
import numpy as np
import csv
data = read_file('dataset_proto.csv')
kana_title, kana_ans = conv_str_to_kana(data[0],data[1])
vec_title = conv_kana_to_vec(kana_title,1,"T")
vec_ans = conv_kana_to_vec(kana_ans,1,"R")
"""
for i,kana_title in enumerate(kana_title):
print(kana_title)
print(vec_title[i])
clf = svm.SVC(gamma=0.001, C=100)
clf.fit(vec_title[:-1], vec_ans[:-1])
result = clf.predict(data[:-1])
print("実際の答え={0}, 予測結果={1}".format(vec_ans[-1], result))
result = clf.score(data, vec_ans)
print(result)
"""
# データセットを学習用とテスト用に分割
X_train, X_test, Y_train, Y_test = train_test_split(vec_title, vec_ans, train_size = 0.8, test_size = 0.2, random_state = 0)
def read_file_test(self):
result = input.read_file(self.path)
print(result[1])
from collections import defaultdict
from gensim import corpora, models, similarities
from pprint import pprint # pretty-printer
# documents = ["Human machine interface for lab abc computer applications",
# "A survey of user opinion of computer system response time",
# "The EPS user interface management system",
# "System and human system engineering testing of EPS",
# "Relation of user perceived response time to error measurement",
# "The generation of random binary unordered trees",
# "The intersection graph of paths in trees",
# "Graph minors IV Widths of trees and well quasi ordering",
# "Graph minors A survey"]
documents = input.read_file("raw-filename.txt", ".txt")
# remove common words and tokenize
stoplist = set('for a of the and to in'.split())
texts = [[word for word in document.lower().split() if word not in stoplist]
for document in documents]
# remove words that appear only once
frequency = defaultdict(int)
for text in texts:
for token in text:
frequency[token] += 1
texts = [[token for token in text if frequency[token] > 1] for text in texts]
# pprint(texts)
dictionary = corpora.Dictionary(texts)
__author__ = 'naveed';
import input;
import model;
import numpy
file_name = input.read_file();
csv_list = input.list_converter(file_name);
name_list = [i[1] for i in csv_list];
stat_list = [i[2] for i in csv_list];
WORD_LENGTH = 5;
VECTOR_SIZE = 80;
training_examples = len(name_list);
feature_count = WORD_LENGTH * VECTOR_SIZE;
empty_vector = list(numpy.zeros(VECTOR_SIZE));
# Splitting name list and converting to unicode
name_list_split = [i.split(';') for i in name_list];
name_list_dec = [[value[2:] for value in row] for row in name_list_split];
name_list_hex = [[hex(int(value)) for value in row] for row in name_list_dec];
name_list_uni = [[unichr(int(value)) for value in row] for row in name_list_dec];
# Truncating names and adding whitespace to make length equal 5
for i in name_list_uni:
while len(i)<5:
i.append(u' ');
if len(i)>5: