def loadFile(self):
print(self.path)
self.dataList = loader.load(self.path)
print(self.dataList[0])
self.calculateE()
self.min = self.dataList[0][0]
self.max = self.dataList[0][-1]
self.savePlot()
self.print_graph()
def get(self):
offset = self.request.get('offset')
if offset == '':
offset = 0
offset = int(float(offset))
(meals, entrees) = loadData.load(offset)
#(meals, entrees) = models.getMealsAndEntrees()
#menus = models.getHomeMenus()
#self.response.out.write(menus)
for m in meals:
self.response.out.write(m.html_string())
for e in entrees:
self.response.out.write(e.html_string())
def showDWT():
path = './bci2003'
subject = 'trainData_DWT'.split(' ')
loadData = load(subNames=subject, path=path)
(x_train, y_train) = loadData.loadTrainDataFromTxt()
d_avg = dict()
for tp in range(2):
tmp_index = indices(y_train, tp)
#print(len(tmp_index)) #show class quantity
tempList = x_train[tmp_index]
fixList = np.sum(tempList, axis=0)
resultList = np.divide(fixList, tempList.shape[0])
d_avg[tp] = resultList
plt.subplot(211)
plt.plot(d_avg[0])
plt.legend(['c3', 'cZ', 'c4'], loc='upper left')
plt.subplot(212)
plt.plot(d_avg[1])
plt.legend(['c3', 'cZ', 'c4'], loc='upper left')
plt.show()
print("end")
def showAveragepower():
path = './bci2003/'
subject = 'trainData'.split(' ')
loadData = load(subNames=subject, path=path, isRand=True)
(x_train, y_train) = loadData.loadTrainDataFromTxt()
d_avg = dict()
for tp in range(2):
tmp_index = indices(y_train, tp)
#print(len(tmp_index)) #show class quantity
tempList = x_train[tmp_index]
fixList = np.sum(tempList, axis=0)
resultList = np.divide(fixList, tempList.shape[0])
d_avg[tp] = resultList
LH = np.array(d_avg[0])
RH = np.array(d_avg[1])
time = np.arange(LH[:, 0].size) / 128
plt.subplot(211)
plt.plot(time, LH)
plt.legend(['c3', 'cZ', 'c4'], loc='upper left')
plt.subplot(212)
plt.plot(time, RH)
plt.legend(['c3', 'cZ', 'c4'], loc='upper left')
plt.show()
import numpy as np
import loadData as ld
import writeToCSV as wc
if __name__ == '__main__':
# path = './result/mistake_rate_0.0.csv'
# data = ld.load(path)
# matrix = np.asmatrix(data, dtype=float)
# print(matrix)
result_path = './result/mistake_rate_'
mistake_rate = ['0.0', '0.05', '0.1', '0.2', '0.3', '0.5']
for mr in mistake_rate:
maxtrix = np.zeros([50, 5], dtype=float)
max_iter = 100
file = result_path + mr + '.csv'
for i in range(0, max_iter):
path = result_path + mr + '_' + str(i) + '.csv'
data = ld.load(path)
maxtrix += np.asmatrix(data, dtype=float)
maxtrix /= max_iter
wc.writecsvByName(maxtrix, file)
Created on Mon Nov 28 20:56:36 2016
@author: xierhacker
"""
from __future__ import print_function, division
import numpy as np
import tensorflow as tf
from scipy.io import loadmat
from scipy.io import savemat
import matplotlib.pyplot as plt
import loadData
#import dp
#load data
train_samples, train_labels = loadData.load("../data/train_32x32")
test_samples, test_labels = loadData.load("../data/test_32x32")
'''
#print the infomation of the data
#you can remove the comment to run this code
print("the shape of train_set:",train_samples.shape)
print("the shape of train_labels:",train_labels.shape[0])
print("type of samples element:",train_samples.dtype)
print("type of labels element:",train_labels.dtype)
print("1-20:\n",train_labels[1:20])
'''
#data transformat
train_samples, train_labels = loadData.transformat(train_samples, train_labels)
test_samples, test_labels = loadData.transformat(test_samples, test_labels)
'''
from keras.layers.recurrent import LSTM
from keras.layers.wrappers import Bidirectional
from keras.optimizers import RMSprop
from keras.callbacks import Callback, ModelCheckpoint
from keras.models import load_model
from keras.utils import plot_model
np.random.seed(1337) # for reproducibility
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
# load train dataset
OUTPUT_SIZE = 2
path = './bci2003'
subject = 'trainData_DWT'.split(' ')
getDataClass = load(subNames=subject, path=path, notEqual='n',isRand=True)
(x_train, y_train) = getDataClass.loadTrainDataFromTxt()
y_train = np_utils.to_categorical(y_train, num_classes=OUTPUT_SIZE)
print('x_train shape:', x_train.shape)
print('y_train shape:', y_train.shape)
# load test dataset
path = './bci2003'
subject = 'testData_DWT'.split(' ')
getDataClass = load(subNames=subject, path=path, notEqual='n',isRand=True)
(x_test, y_test) = getDataClass.loadTrainDataFromTxt()
print('x_test shape:', x_train.shape)
print('y_test shape:', y_train.shape)
BATCH_SIZE = 16
EPOCHS = 200
TIME_STEPS = x_train.shape[1]
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from loadData import load from sklearn.linear_model import LogisticRegression as LR def logisticRegressionTrain(trainData, trainLabels, solver="liblinear"): model = LR(solver=solver) model.fit(trainData, np.array(trainLabels).ravel()) return model def logisticRegressionEvaluate(model, data, labels): predictions = model.predict(data) errors = (predictions != np.array(labels).ravel()) return 100 * np.mean(errors) if __name__ == '__main__': trainData, trainLabels, testData, testLabels = load() model = logisticRegressionTrain(trainData, trainLabels) trainError = logisticRegressionEvaluate(model, trainData, trainLabels) print "Training Error: " + trainError testError = logisticRegressionEvaluate(model, testData, testLabels) print "Test Error: " + testError
# -*- coding:utf-8 -*- #对分类问题进行学习拟合 import math import random import numpy as np import scipy.optimize as op import matplotlib.pyplot as plt import time from loadData import load #--导入训练数据 data = load(r"F:\SomeMLProjects\ex2data1.txt") #data前两列为x 最后一列为y 注意要将y化为矩阵形式 x = np.c_[data[:, 0], data[:, 1]] y = np.array([data[:, 2]]).T # 以当前时间戳的int为随机数种子 np.random.seed(int(time.time())) # 对于只有一行的矩阵 np.random.randn输出的也会是个数组中的数组 # theta个数与单个输入x的维度一样 此处1表示列长度 m = np.size(x, 1) theta = np.random.randn(m, 1) #print(theta) testX = [] testY = [] J_history = [] i_history = []
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM, BatchNormalization, Conv1D, MaxPooling1D
import matplotlib.pyplot as plt
import loadData
import preprocess
from sklearn import preprocessing
import pandas as pd
df = loadData.load()
# features=len(df.columns)
training_size = 0.8
spilt_point = int(training_size * len(df))
#splitting data for training and testing in ratio 8:2
train_df = df[:spilt_point]
test_df = df[spilt_point:]
print(f"train_df {train_df[:10]}")
print(f"test_df {test_df[:10]}")
train_x, train_y = preprocess.process_data(train_df)
test_x, test_y = preprocess.process_data(test_df)
# print(f"train_x.shape[1:]{train_x.shape[1:]}")
NAME = "NIFTY50PRED"
args = parser.parse_args()
# setup correct name
args.save_name = args.sampling + "_" + str(args.no_lines) + "lines_" + str(
args.seed) + "seed"
# set up the gpu
args.device = cudaDeviceChecker.device(args)
# put a limit on cpu resources used by pytorch
torch.set_num_threads(8)
torch.random.manual_seed(args.seed)
# load data
dataloader_train, dataloader_val, dataloader_test = loadData.load(args)
# %% create network depending on the type of sampling performed
if args.sampling in ["random_uniform", "low_pass", "VDS", "GMS"]:
Network = myModel.fixedMask(args)
if args.sampling == "LOUPE":
Network = myModel.LOUPEMask(args)
if args.sampling == "DPS":
Network = myModel.DPSMask(args)
if args.sampling == "ADPS":
Network = myModel.ADPSMask(args)
Network = Network.to(args.device)
# discriminator network
Discriminator = discriminatorModel.Discriminator(args)
def cluster():
# INITIALIZE PARAMETERS
y, dates = loadData.load()
sampling_rate = 1
start = 400
end = 1500
eps = 26
min_samples = 25
threshold = 0.9
branching_factor = 5
# CUSTOMIZE TRAIN DATA
array = []
for i in range(151):
# x = y[i][0]
x = y[0][i]
# x = x[2000:4000]
x = x[start:end:sampling_rate]
array.append(x)
X_train = np.array(array)
X_train = preprocessing.scale(X_train, axis=1)
array = []
for x in X_train:
x = x.flatten()
fs = float(end - start)
fc = float(20)
w = float(fc / (fs / 2))
b, a = signal.butter(5, w, 'low')
xx1 = signal.filtfilt(b, a, x)
array.append(xx1)
X_train = np.array(array)
print(np.linalg.norm(X_train[15] - X_train[14]))
# nbrs = NearestNeighbors(n_neighbors=len(X_train)).fit(X_train)
# distances, indices = nbrs.kneighbors(X_train)
# # print(distances.flatten().shape)
# # print(distances.shape)
# sortedDiss = distances.flatten()
# sortedDiss = np.sort(sortedDiss)
# index = np.arange(0,22801)
# plt.plot(sortedDiss, index)
# plt.ylabel('indices')
# plt.xlabel('distance')
# plt.show()
# clustering = OPTICS(min_samples=5).fit(X_train)
clustering = DBSCAN(eps=eps, min_samples=min_samples).fit(X_train)
# clustering = KMeans(n_clusters=3, random_state=0).fit(X_train)
# clustering = AgglomerativeClustering(n_clusters=3).fit(X_train)
# clustering = Birch(branching_factor=branching_factor, n_clusters=3, threshold=threshold, compute_labels=True).fit(X_train)
for n, i in enumerate(clustering.labels_):
if i == -1:
clustering.labels_[n] = 2
y_pred = clustering.labels_
sink_indices = [u'040928', u'050606', u'061211']
source_indices = [u'071004']
for i in range(151):
if dates[i][0] in sink_indices:
y_pred[i] = 1
if dates[i][0] in source_indices:
y_pred[i] = 0
print(type(y_pred))
append_title = "DBSCAN Algorithm with minSamples={}, eps={}".format(
min_samples, eps)
# fcluster2 = filter_data.filter(X_train, 50, 6)
plots.plotCluster(0,
y_pred,
X_train,
dates,
sampling_rate,
end,
start,
append_title,
analyse=True)
# plots.plotCluster(1, y_pred, X_train, dates, sampling_rate, end, start, append_title, analyse=True)
# plots.plotCluster(2, y_pred, X_train, dates, sampling_rate, end, start, append_title)
# plots.plotCluster(2, y_pred, fcluster2, dates, sampling_rate, end, start, append_title)
return y_pred, X_train
dataList[0] = tempRange
dataList[1] = dCInterp
return dataList
def drawPlot(x, y):
plot.xlabel("Temp")
plot.ylabel("Entalphy")
plot.plot(x, y)
plot.show()
if (__name__ == "__main__"):
filename = "dane.txt"
dataFolderPath = "resources/"
path = dataFolderPath + filename
dataList = loader.load(path)
dataList = interpolate(dataList)
resultList = calculateE(dataList[0], dataList[1])
p = Process(target=drawPlot, args=(dataList[0], resultList))
p.start()
for x in range(len(dataList[0])):
print("Temp: {}\tdC: {}\t Result: {}".format(dataList[0][x],
dataList[1][x],
resultList[x]))
p.join()
import pandas as pd
import numpy as np
import os
import loadData as ld
dir_path = ld.dir_path
train = ld.load('train.csv')
userFeatures = ld.load('user.csv')
data = pd.merge(train.head, userFeatures, on=['userID'], how='left')
print data.head(10)
#data.to_csv(r'data.csv', encoding='gbk')
import loadData as load
import pandas as pd
from sklearn import linear_model as lin
import numpy as np
traincsv = load.load('train100.csv')
reg = linear_model.LinearRegression()
from keras.models import load_model
from keras.utils import np_utils
from loadData import load
X_train, y_train, X_test, y_test = load()
model = load_model("my_model.h5")
score = model.predict(X_test)
c = 0
for i in range(10000):
pos = 0
maxEle = -1.00
for j in range(len(score[i])):
if(score[i][j] > maxEle):
maxEle = score[i][j]
pos = j
if(pos == y_test[i]):
c += 1
print((c / 10000) * 100)
# setup correct name
args.save_name = args.sampling + "_" + str(args.no_lines) + "lines_" + str(
args.seed) + "seed"
if args.Pineda == True:
args.save_name += "_Pineda"
# set up the gpu
args.device = cudaDeviceChecker.device(args)
# put a limit on cpu resources used by pytorch
torch.set_num_threads(8)
torch.random.manual_seed(args.seed)
# load data
_, _, dataloader_test = loadData.load(args, only_test=True)
# create network depending on the type of sampling performed
if args.sampling in ["random_uniform", "low_pass", "VDS", "GMS"]:
Network = myModel.fixedMask(args)
if args.sampling == "LOUPE":
Network = myModel.LOUPEMask(args)
if args.sampling == "DPS":
Network = myModel.DPSMask(args)
if args.sampling == "ADPS" and args.Pineda == False:
Network = myModel.ADPSMask(args)
if args.sampling == "ADPS" and args.Pineda == True:
Network = myModel.ADPSMask_legacy(args)
Network = Network.to(args.device)
mode = 'rgb'
else:
mode = 'opt'
model = Inception_Inflated3d(input_shape=input_shape, dropout_prob=0.5, data_format=args.data_format,mode=mode, nclass=args.nclass )
model.summary()
print("Number of layer in model : ", len(model.layers))
model.compile(loss=categorical_crossentropy, optimizer=Adam(), metrics=['accuracy'])
#-------------------------------------------LOAD DATA TO TRAINNING-----------------------------------------
print("[INFOR] : Starting loading data form disk...........")
# color: True RGB
# color: False OPT Flow RGB
from loadData import load
(X_train, y_train, X_test, y_test) = load(mode = 'opt')
print("[INFOR] : Loading Done !")
from sklearn.preprocessing import LabelBinarizer
label_binary = LabelBinarizer()
y_train = label_binary.fit_transform(y_train)
print(label_binary.classes_)
y_train = np.asanyarray(y_train)
y_test = label_binary.transform(y_test)
y_test = np.asanyarray(y_test)
# #------------------------------------------TRAINNING-------------------------------------------------------
args.no_pixels = int(28**2 * args.percentage / 100)
# setup correct name
args.save_name = args.sampling + "_" + str(
args.percentage) + "percent_" + str(args.seed) + "seed"
# set up the gpu
args.device = cudaDeviceChecker.device(args)
# put a limit on cpu resources used by pytorch
torch.set_num_threads(8)
torch.random.manual_seed(args.seed)
# load data
train_loader, val_loader, test_loader = loadData.load(args)
# %% create network depending on the type of sampling performed
Network = myModel.Network(args)
Network = Network.to(args.device)
# optimizers
if args.sampling == 'DPS':
optimizer = optim.Adam([
{
'params': Network.f1.parameters(),
'lr': args.learning_rate
},
{
'params': Network.f2.parameters(),
'lr': args.learning_rate
import matplotlib.pyplot as plt
from scipy.io import loadmat, savemat
import numpy as np
from scipy import signal
import pywt
import umap
from sklearn.cluster import DBSCAN, KMeans
import copy
from tslearn.preprocessing import TimeSeriesScalerMeanVariance, TimeSeriesResampler
import sys
sys.path.insert(1, '../')
import loadData
y, dates = loadData.load()
eps = 24.5
min_samples = 27
sampling_rate = 1.5
start = 400
end = 1500
array = []
for i in range(151):
x = y[i][0]
x = x[2000:4000]
x = x[start:end:1]
array.append(x)
X_train = np.array(array)
X_train = TimeSeriesScalerMeanVariance().fit_transform(X_train[:151])
