def update_files():
input_data = []
for i in range(1, 6):
with open(f'in\\{str(i)}.csv', encoding='utf-8', newline='') as f_in:
for row in csv_read(f_in):
row.insert(0, companies[i])
input_data.append(row)
# generating output data
output_data = {1: [], 2: [], 3: []}
for line in input_data:
if 'производитель 1' in line:
output_data[1].append(line[:3] + line[4:])
elif 'производитель 3' in line:
output_data[2].append(line[:3] + line[4:])
if 'яблоки' in line:
output_data[3].append(line[:1] + line[2:])
for i in range(1, 4):
with open(f'out\\{str(i)}.csv', 'w', encoding='utf-8',
newline='') as f_out:
csv_write(f_out).writerows(output_data[i])
return None
# from sklearn.linear_model import LogisticRegression
# from sklearn.neural_network import MLPClassifier
from sklearn import svm
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
print("Import finished")
parameter_num = 15 # the number of mfcc's parameters
train_pos_num = 100
train_neg_num = 100
test_num = 100
dataset_root = 'D:/dataset'
train_label = np.array([1] * train_pos_num + [0] * train_neg_num)
test_label = [0] * test_num
csv_list_test = csv_read(
open('D:/dataset/test_result.csv', 'r', encoding='utf-8'))
csv_list_test = list(csv_list_test)[0]
csv_list_test[0] = csv_list_test[0].strip('\ufeff')
for i in range(test_num):
test_label[i] = int(csv_list_test[i])
test_label = np.array(test_label)
# 1.图像特征向量读取
image_read_flag = 0
image_train_pos_root = './image_feat/train_pos.npy'
image_train_neg_root = './image_feat/train_neg.npy'
image_test_root = './image_feat/test.npy'
if not os_path_exists('./image_feat/'):
makedirs('./image_feat/')
image_read_flag = 1
# import matplotlib.pyplot as plt
# from sklearn.model_selection import train_test_split, learning_curve, validation_curve
from sklearn.preprocessing import StandardScaler, PolynomialFeatures # , Normalizer
from sklearn.neural_network import MLPClassifier
print("Import finished")
parameter_num = 15 # the number of mfcc's parameters
train_pos_num = 100
train_neg_num = 100
test_num = 100
dataset_root = 'D:/Temp/Matlab/Machine_Learning/dataset'
train_label = np.array([1] * train_pos_num + [0] * train_neg_num)
test_label = [0] * test_num
csv_list_test = csv_read(
open('D:/Temp/Matlab/Machine_Learning/dataset/test_result.csv',
'r',
encoding='utf-8'))
csv_list_test = list(csv_list_test)[0]
csv_list_test[0] = csv_list_test[0].strip('\ufeff')
for i in range(test_num):
test_label[i] = int(csv_list_test[i])
test_label = np.array(test_label)
# 1.图像特征向量读取
image_read_flag = 0
image_train_pos_root = './image_feat/train_pos.npy'
image_train_neg_root = './image_feat/train_neg.npy'
image_test_root = './image_feat/test.npy'
if not os_path_exists('./image_feat/'):
makedirs('./image_feat/')
image_read_flag = 1
def csv_write(self, pfadname, delimiter):
"""
Eine Methode zum Schreiben von CSV-Dateien
"""
ifile = open(self.filename) #Oeffnen des vorhandenen Files
sn = csv.Sniffer()
dialect = sn.sniff(ifile.read(1024))
reader = csv.reader(ifile, dialect)
ofile = open(pfadname, "w") #Oeffnen des Files, in welches hineingeschrieben werden soll
writer = csv.writer(ofile, delimiter=delimiter, quotechar='"', quoting=csv.QUOTE_ALL) #Setzen des Writers
for row in reader:
writer.writerow(row) #Schreiben in das File
#Schliessen der beiden Dateien
ifile.close()
ofile.close()
if __name__ == "__main__":
"""
Definition der Main, Aufrufen der Read und Write Methoden
"""
csv = CSVreader("WahlKopie.csv") #Angeben der Datei
read = csv.csv_read() #Lesen der Datei
csv.csv_write("WahlNeu.csv", '\t') #Schreiben in ein neues File
#print (read) #Ausgeben des Eingelesenen
from csv import reader as csv_read
import numpy as np
from utils.general_plotting import smooth_histogram
from matplotlib import pyplot as plt
from scipy.stats import ks_2samp
from utils.linalg import moving_average
master_table = []
with open(my_file, 'r') as source:
reader = csv_read(source)
header = reader.next()
for line in reader:
line = [float(elt) if elt else np.nan for elt in line[1:-3]]
master_table.append(line)
master_table = np.array(master_table)
D_r = master_table[master_table[:, -1] > 0., 1]
D_nr = master_table[master_table[:, -1] == 0., 1]
GM_r = master_table[master_table[:, -1] > 0., 0]
GM_nr = master_table[master_table[:, -1] == 0., 0]
S_r = master_table[master_table[:, -1] > 0., 2]
S_nr = master_table[master_table[:, -1] == 0., 2]
s_r = master_table[master_table[:, -1] > 0., -2]
s_nr = master_table[master_table[:, -1] == 0., -2]
s_3d_av = moving_average(
master_table[np.logical_not(np.isnan(master_table[:, -1])), -2], 3)