def convertnctotiff(self):
self.cancelbtn.config(state="disabled")
matlabpath = "\"C:/Program Files/MATLAB/R2016b/bin/matlab\""
mlab = Matlab(executable=matlabpath)
ncfilefullpath = self.inputncdirtxtfield.get("1.0",
tk.END).encode("ascii")
ncfilefullpath = ncfilefullpath[0:-1]
#ncfilefullpathst = 'D:/NARSS/Research Project/2018-2019/01-01-2020/Task_NC-2-TIFF/input/GRCTellus.JPL.200204_201603.GLO.RL05M_1.MSCNv02CRIv02.nc'
#print('ncfilefullpathdyn: '+ncfilefullpath, type(ncfilefullpath))
tiffoutputdir = self.outputtiffdirtxtfield.get("1.0",
tk.END).encode("ascii")
tiffoutputdir = tiffoutputdir[0:-1]
tiffoutputdir = os.path.join(tiffoutputdir, '')
#tiffoutputdirst = 'D:/NARSS/Research Project/2018-2019/01-01-2020/Task_NC-2-TIFF/output/'
#print('tiffoutputdir: '+tiffoutputdir, type(tiffoutputdir))
ncvar = self.ncvartxtfield.get("1.0", tk.END).encode("ascii")
ncvar = ncvar[0:-1]
#ncvar = 'lwe_thickness'
#print('ncvar: '+ncvar, type(ncvar))
nctimes = self.nctimestxtfield.get("1.0", tk.END)
nctimes = int(nctimes)
#nctimes = 152
#print('nctimes: '+nctimes, type(nctimes))
mlab.start()
mlab.run_func(
'C:/Users/akotb/PycharmProjects/AGP_System/resources/netcdf_to_tiff.m',
{
'arg1': ncfilefullpath,
'arg2': ncvar,
'arg3': nctimes,
'arg4': tiffoutputdir
})
#self.conversionstartedlbl.grid_forget()
self.startconvertingnctotiffbtn.grid_forget()
self.cancelbtn.grid_forget()
# task status
self.conversioncompletedlbl = tk.Label(self.master,
text="Conversion Completed")
self.conversioncompletedlbl.grid(sticky='W',
padx=10,
pady=10,
row=5,
column=1)
self.closebtn = tk.Button(self.master,
text="Close",
width=15,
command=self.exit)
self.closebtn.grid(sticky='E', padx=10, pady=10, row=5, column=2)
mlab.stop()
def fix_model(self,
W,
intMat,
drugMat,
targetMat,
num,
cvs,
dataset,
seed=None):
self.dataset = dataset
self.num = num
self.cvs = cvs
self.seed = seed
R = W * intMat
drugMat = (drugMat + drugMat.T) / 2
targetMat = (targetMat + targetMat.T) / 2
mlab = Matlab()
mlab.start()
# print os.getcwd()
# self.predictR = mlab.run_func(os.sep.join([os.getcwd(), "kbmf2k", "kbmf.m"]), {'Kx': drugMat, 'Kz': targetMat, 'Y': R, 'R': self.num_factors})['result']
res = mlab.run_func(
os.path.realpath(os.sep.join(['kbmf2k', "kbmf.m"])), {
'Kx': drugMat,
'Kz': targetMat,
'Y': R,
'R': self.num_factors
})
self.predictR = res['result']
# print os.path.realpath(os.sep.join(['../kbmf2k', "kbmf.m"]))
mlab.stop()
def tucker(self, coreNway, lam, init=None):
# 初始化
if init is None:
A0, C0 = self.tucker_init(*coreNway)
else:
assert len(init) == 4
A0 = init[:3]
C0 = init[-1]
mlab = Matlab()
mlab.start()
while True:
res = mlab.run_func(
'tucker.m', self.R, coreNway, {
'hosvd': 1,
'lam': lam,
'maxit': 4000,
'A0': A0,
'C0': C0,
'tol': 1e-5
})
if res['success']:
break
mlab.stop()
O, D, T, C = res['result']
return TuckerResult(self.R, O, D, T, C)
def decision(data):
image=data.save_to_disk('/home/user1/Downloads/Carla9.5/PythonAPI/my projects/camera_result/%06d.jpg' % data.frame_number);
print('this is the path to send to matlab: ',image);
mlab = Matlab()
mlab.start()
overlap = mlab.run_func('parking.m', {'img': image})
return overlap
def start_mlab():
dir_path = os.path.dirname(os.path.realpath(__file__))
dir_list = ['/HHT_MATLAB_package', '/HHT_MATLAB_package/EEMD',
'/HHT_MATLAB_package/checkIMFs', '/HHT_MATLAB_package/HSA']
mlab = Matlab()
mlab.start()
for d in dir_list:
res = mlab.run_func('addpath', dir_path + d)
return mlab
def network_hill(panel, prior_graph=[], lambdas=[], max_indegree=3, reg_mode='full', stdise=1, silent=0, maxtime=120):
'''
run_hill(panel)
input: dataframe
should be a T x N dataframe with T time points and N samples.
output: dict containing key 'e' and key 'i' from Hill's code
'''
from scipy.io import savemat
from scipy.io import loadmat
# start matlab
mlab = Matlab(maxtime=maxtime)
mlab.start()
# .mat shuttle files
# add path check
inPath = os.path.join('..', 'cache', 'dbn_wrapper_in.mat')
outPath = os.path.join('..', 'cache', 'dbn_wrapper_out.mat')
D = np.transpose(panel.values)
num_rows = np.shape(D)[0]
num_cols = np.shape(D)[1]
D = np.reshape(D, (num_rows, num_cols, 1))
#D = np.transpose(panel, (2,1,0))
# save the matlab object that the DBN wrapper will load
# contains all the required parameters for the DBN code
savemat(inPath, {"D" : D,
"max_indegree" : max_indegree,
"prior_graph" : prior_graph,
"lambdas" : lambdas,
"reg_mode" : reg_mode,
"stdise" : stdise,
"silent" : silent})
# DBN wrapper just needs an input and output path
args = {"inPath" : inPath, "outPath" : outPath}
# call DBN code
res = mlab.run_func('dbn_wrapper.m', args, maxtime=maxtime)
mlab.stop()
out = loadmat(outPath)
edge_prob = pd.DataFrame(out['e'], index=panel.columns, columns=panel.columns)
edge_sign = pd.DataFrame(out['i'], index=panel.columns, columns=panel.columns)
#edge_prob = out['e']
#edge_sign = out['i']
return (edge_prob, edge_sign)
def mask_to_region(self, path):
"""
Converts mask image into corresponding regions list
Arguments
---------
path : string
Path to mask image
Returns
-------
regions : list
"regions" for the dataset that the mask belongs to
"""
def remove_interiors(L, region):
"""
Removes interior pixels in neuron regions
Arguments
---------
L : 2D numpy array
Matrix containing labels for each neuron
region : list
List of all pixels in a neuron label
"""
for pixel in region:
# Creating grid around pixel
grid = L[pixel[0] - 1:pixel[0] + 2, pixel[1] - 1:pixel[1] + 2]
# Removing pixels which are surrounded by similar values
if np.unique(grid).size == 1:
region.remove(pixel)
return region
# Initializes Matlab to get labels for neurons using bwboundaries() method
cwd = os.getcwd()
matlab_file_path = os.path.join(cwd, 'utils',
'get_region_boundaries.m')
mlab = Matlab()
mlab.start()
matlab_result = mlab.run_func(matlab_file_path, {'arg1': path})
mlab.stop()
# `L` is 2D array with each neuron carrying a label 1 -> n
L = matlab_result['result']
n = int(L.max()) # Number of neurons
# Getting coordinates of pixels of neuron regions
# This includes interior pixels as well
regions = [{
"coordinates": list(zip(*np.where(L == float(i))))
} for i in range(1, n)]
# Removing interior pixels in neuron regions
for region in regions:
remove_interiors(L, region["coordinates"])
return regions
def start_mlab():
dir_path = os.path.dirname(os.path.realpath(__file__))
dir_list = [
'/HHT_MATLAB_package', '/HHT_MATLAB_package/EEMD',
'/HHT_MATLAB_package/checkIMFs', '/HHT_MATLAB_package/HSA'
]
mlab = Matlab()
mlab.start()
for d in dir_list:
res = mlab.run_func('addpath', dir_path + d)
return mlab
def fix_model(self, W, intMat, drugMat, targetMat, seed=None):
R = W*intMat
drugMat = (drugMat+drugMat.T)/2
targetMat = (targetMat+targetMat.T)/2
mlab = Matlab()
mlab.start()
# print os.getcwd()
# self.predictR = mlab.run_func(os.sep.join([os.getcwd(), "kbmf2k", "kbmf.m"]), {'Kx': drugMat, 'Kz': targetMat, 'Y': R, 'R': self.num_factors})['result']
self.predictR = mlab.run_func(os.path.realpath(os.sep.join(['../kbmf2k', "kbmf.m"])), {'Kx': drugMat, 'Kz': targetMat, 'Y': R, 'R': self.num_factors})['result']
# print os.path.realpath(os.sep.join(['../kbmf2k', "kbmf.m"]))
mlab.stop()
def plot_deltahist(fileName1, fileName2):
prog = find_executable('matlab')
if not prog:
sys.exit("Could not find MATLAB on the PATH. Exiting...")
else:
print("Found MATLAB in "+prog)
mlab = Matlab(executable=prog)
mlab.start()
results = mlab.run_func('./plot_deltahist.m', {'arg1': fileName1, 'arg2': fileName2})
mlab.stop()
def fix_model(self, W, intMat, drugMat, targetMat,num, cvs, dataset, seed=None):
self.dataset = dataset
self.num = num
self.cvs = cvs
self.seed = seed
R = W * intMat
#R = np.transpose(R)
drugMat = (drugMat + drugMat.T) / 2
targetMat = (targetMat + targetMat.T) / 2
mlab = Matlab()
mlab.start()
res = mlab.run_func(os.path.realpath(os.sep.join(['GRMF',"runGRMF.m"])),{'Kx': drugMat, 'Kz': targetMat, 'Y': R, 'cv': self.cv})
self.predictR = res['result']
mlab.stop()
1+1
def fix_model(self, W, intMat, drugMat, targetMat, seed=None):
R = W * intMat
drugMat = (drugMat + drugMat.T) / 2
targetMat = (targetMat + targetMat.T) / 2
mlab = Matlab('/Applications/MATLAB_R2014b.app/bin/matlab')
mlab.start()
# print os.getcwd()
# self.predictR = mlab.run_func(os.sep.join([os.getcwd(), "kbmf2k", "kbmf.m"]), {'Kx': drugMat, 'Kz': targetMat, 'Y': R, 'R': self.num_factors})['result']
self.predictR = mlab.run_func(
os.path.realpath(os.sep.join(['./kbmf2k', "kbmf.m"])), {
'Kx': drugMat,
'Kz': targetMat,
'Y': R,
'R': self.num_factors
})['result']
# print os.path.realpath(os.sep.join(['./kbmf2k', "kbmf.m"]))
mlab.stop()
def call_peak_picking(filepath, scriptpath):
""" Method connects to local Matlab via ZeroMQ and calls peak picking function there implemented in script path. """
start_time = time.time()
mlab = Matlab(
executable='/Applications/MATLAB_R2018a_floating.app/bin/matlab')
mlab.start()
response = mlab.run_func(scriptpath, {'path': filepath})
mlab.stop()
peaks = response['result']
print(time.time() - start_time, " seconds elapsed for peak picking")
print("Total number of peaks", len(peaks))
return peaks
def objective_function(self, x):
'''
matlabpath: we need the path to matlab since we need to run it.
IMPORTANT: walker_simulation.m must be included in the
WGCCM_three_link_walker_example file in order to work.
So simply modify the path below.
'''
mlab = Matlab(executable= matlabpath)
mlab.start()
output = mlab.run_func(os.path.join(self.matlab_code_directory, 'walker_simulation.m'),
{'arg1': x[:, 0],'arg2': x[:, 1],'arg3': x[:, 2],
'arg4': x[:, 3],'arg5':x[:, 4],'arg6': x[:, 5],
'arg7': x[:, 6],'arg8': x[:, 7],'arg9': self.num_steps})
answer = output['result']
simul_output = answer['speed']
mlab.stop()
def evaluation(self, test_data, test_label):
mlab = Matlab()
mlab.start()
res = mlab.run_func(
os.path.realpath(os.sep.join([os.getcwd(), "pudt", "PUDT.m"])), {
'w': self.w,
'dataset': self.dataset
})
self.predictR = res['result']
mlab.stop()
# score = self.predictR[test_data[:, 0], test_data[:, 1]]
score = self.predictR
import pandas as pd
score = pd.DataFrame(score)
score.to_csv('../data/datasets/EnsambleDTI/pudt_' + str(self.dataset) +
'_s' + str(self.cvs) + '_' + str(self.seed) + '_' +
str(self.num) + '.csv',
index=False)
prec, rec, thr = precision_recall_curve(np.array(score['test_label']),
np.array(score['score']))
aupr_val = auc(rec, prec)
# plt.step(rec, prec, color='b', alpha=0.2,
# where='post')
# plt.fill_between(rec, prec, step='post', alpha=0.2,
# color='b')
#
# plt.xlabel('Recall')
# plt.ylabel('Precision')
# plt.ylim([0.0, 1.05])
# plt.xlim([0.0, 1.0])
# plt.title('2-class Precision-Recall curve: AP={0:0.2f}')
fpr, tpr, thr = roc_curve(np.array(score['test_label']),
np.array(score['score']))
auc_val = auc(fpr, tpr)
print("AUPR: " + str(aupr_val) + ", AUC: " + str(auc_val))
return aupr_val, auc_val
def Run(C, R, mic, Plot):
CHUNK = 44100 # number of data points to read at a time 4096
CHUNK = C
# 4096 byte
# the number of frames
RATE = 44100 # 176400 # time resolution for reading device (Hz) 44100 samples/second
RATE = R
# sampling rate i.e the number of frames per second
serSignal = 'S'
KnockSignal = 'K'
Input_Device_Index = 2
Input_Device_Index = mic
plot = Plot
# Define the serial port
ser_port = "COM8" # for window computer, int must be used COM1 = 0,COM2=1 ...
baud_rate = 9600
count = 0
flag = False
signal = False
mlab = Matlab(executable=r"D:\MATLAB\bin\matlab.exe")
mlab.start()
p = pyaudio.PyAudio()
# while True:
# ser.write(serSignal.encode('utf-8'))
# if ser.readline().decode('utf-8') != "Spray":
# break
stream = p.open(format=pyaudio.paInt16,
channels=1,
rate=RATE,
input=True,
input_device_index=None,
frames_per_buffer=CHUNK)
ser = serial.Serial(ser_port, baud_rate)
print(ser.readline().decode("utf-8"))
print("Input delay is %f" % stream.get_input_latency())
while (True):
for i in range(int(3)): #only loop forA int(??) times
#if(count>1):
# sleep(1)
if (count == 1):
ser.write(KnockSignal.encode(
"utf-8")) # encode is used for string.encode()
sleep(.32) # **change here (0.1s per 5000samples)
flag = True
print("Must Knock Here")
# The input device id "2" => built-in microphone
# info = p.get_host_api_info_by_index(0)
# numdevices = info.get('deviceCount')
# for i in range(0, numdevices):
# if (p.get_device_info_by_host_api_device_index(0, i).get('maxInputChannels')) > 0:
# pass
#print('Input Device id', i, '-', p.get_device_info_by_host_api_device_index(0, i).get('name'))
# get the default device info
#print(p.get_default_input_device_info())
# create a numpy array holding a single read of audio data
#now = datetime.now()
if flag == True:
# if count ==1:
# sleep(.5)
np.set_printoptions(threshold=sys.maxsize)
data = np.fromstring(stream.read(CHUNK), dtype=np.short)
#print(stream)
time = np.arange(0, CHUNK)
#peak=np.average(np.abs(data))*21
#bars="#"*int(50*peak/2**16)
#print("%04d %s"%(i,data))
#print("%s %s" % (data/32768,now ))
#print("Input data is ", type(data))
# Test Matlab data 1
#res = mlab.run_func('jk.m', {'arg1': data})
#print("Output data is ", type(res['result']))
#data1 = res['result'] # The data in matlab is float64 (e.g for 64bit window) https://stackoverflow.com/questions/8855574/convert-ndarray-from-float64-to-integer
#M_data1 = data1[0] / 32768
#print("jk.m is",res)
# data1 = np.array(res['result'], dtype=np.float64).astype(np.int64)
# print(type(data1))
#Write data to text file before matlab
# with open("SignalTest1.txt", "wt") as file:
# file.write("%s" % (str(M_data1).lstrip('[').rstrip(']')))
# file.flush()
# file.close()
# # file.writelines("%s %04d %s\n"%(now,i,data))
# # close the stream gracefully
# max_val =np.amax(data)
# print(max_val)
# if max_val >30000:
#data/32768
#print(M_data1)
if count == 1:
print("Write")
with open("SignalTest.txt", "wt") as out_file:
out_file.writelines(
str(data)) #it can only write string
if plot == True and count == 2:
past = stream.get_time()
np.set_printoptions(threshold=sys.maxsize)
data = np.fromstring(stream.read(CHUNK), dtype=np.short)
present = stream.get_time()
delay = present - past
print("The delay is %f" % delay)
plt.title('AudioSample')
plt.plot(time, data)
plt.ylim(-40000, 40000)
plt.ylabel('Amplitude')
plt.xlabel('Sample Size')
#plt.pause(.0000000000000000000000000000000000000000000000000000000001)
#plt.clf()
#print(stream.get_time())
dataprocess = mlab.run_func(
'final_judge.m', {"arg1": data}) # ,{'arg1':data}
# print("The input data is ",M_data1)
print(np.amax(data))
print(dataprocess['result'])
d1 = dataprocess['result']
if d1 == 1:
ser.write(serSignal.encode(
"utf-8")) # encode is used for string.encode()
# print(ser.write(serSignal.encode("utf-8")))
#print(ser.readline().decode("utf-8"))
#d1 = 2
plt.show()
flag = False
count = 0
count += 1
#ser.reset_output_buffer()
mlab.stop()
out_file.close()
stream.stop_stream()
stream.close()
p.terminate()
sys.exit(0)
def backtest(filename, data, schedule_data):
f = open(filename)
f.readline()
player_data = {}
time_data = []
for i in xrange(50):
line = f.readline()
if line is None or len(line) == 0:
break
date = int(line[:3])
print date
jsonvalue = "{"+f.readline()+"}"
value = json.loads(jsonvalue)
time_data.insert(0,(date,value))
for p in value:
if not p in player_data:
player_data[p] = [0]
player_data[p].insert(0,value[p])
time_data2 = convertToPlayersTimeData(time_data, data)
teams = set([i.team for i in data])
for i in xrange(len(time_data2)):
stamp_data = time_data2[i][1]
Tracer()()
portfolio = ["rohit sharma", "ajinkya rahane", "david warner", "glenn maxwell", "robin uthappa", "shane watson", "sandeep sharma", "sunil narine", "pravin tambe", "yuzvendra chahal", "bhuvneshwar kumar"]
# portfolio = ["yuzvendra chahal", "shakib al hasan", "shane watson", "rohit sharma", "sandeep sharma", "sunil narine", "ajinkya rahane", "jacques kallis", "robin uthappa", "jayant yadav","bhuvneshwar kumar"]
# portfolio = ["manish pandey", "rohit sharma","jacques kallis","robin uthappa", "aditya tare", "ambati rayudu", "morne morkel","piyush chawla","sunil narine","lasith malinga","pragyan ojha"]
power_player = "glenn maxwell"
# power_player = "bhuvneshwar kumar"
portfolio_p = set([getPlayer(data, p)[0] for p in portfolio])
power_player_p = getPlayer(data, power_player)[0]
points = 0
subs = 75
mlab = Matlab(matlab='/Applications/MATLAB_R2013a.app/bin/matlab')
mlab.start()
for i in xrange(4,len(time_data2)):
# START = str(time_data2[i][0])
# CURRENT_TEAM = set(portfolio)
# SUBSTITUTIONS = subs
# PAST_STATS = time_data[i][1]
print "\n\n\n\n\n\n"
print (subs, str(time_data2[i][0]))
print set(portfolio_p)
print points
print "\n\n\n\n\n\n"
# print time_data[i-1][1]
# Tracer()()
inp = (subs, str(time_data2[i][0]), set(portfolio), time_data[i-1][1])
backtest_pickteam.pickTeam(data, schedule_data, inp)
res = mlab.run_func('/Users/deedy/Dev/FantasyIPL-Moneyball/python2matlab.m', {}, maxtime = 500)
changes = backtest_results.getResults(data, schedule_data, res['result'])
subs -= changes[2]
portfolio_p = changes[0]
power_player_p = changes[1]
# Tracer()()
# update portfolio
# update subs
# update power player
# Tracer()()
teams = [(p,time_data2[i][1][p] - time_data2[i-1][1][p] ) for p in time_data2[i][1] if p in portfolio_p]
print teams
pthis = 0
for i in teams:
if power_player_p == i[0]:
pthis += 2*i[1]
else:
pthis += i[1]
points+= pthis
print "{0}\t{1}\t{2}\n\n".format(points, pthis, subs)
# print "{0}\t{1}".format(time_data2[i][0] , teams)
mlab.stop()
Tracer()()
f.close()
times which emulates a counter. Once it is done with
counting it shuts down the matlab server.
'''
from pymatbridge import Matlab
from distutils.spawn import find_executable
mcruns = 800
res = {'result': 0}
# Search for matlab
prog = find_executable('matlab')
if not prog:
sys.exit("Could not find MATLAB on PATH. Exiting...")
else:
print("Found MATLAB in "+prog)
# Create matlab session
mlab = Matlab(executable=prog)
mlab.start()
# Call jk.m in a for loop to emulate a counter
for mcindex in range(mcruns):
res = mlab.run_func('../matlab/adder.m', {'arg1': res['result'], 'arg2': 1})
print(res['result'])
# Shut down the matlab server
mlab.stop()
from pymatbridge import Matlab
import os
import scipy.io
if __name__ == "__main__":
config = {}
execfile("settings.conf", config)
# python 3: exec(open("example.conf").read(), config)
mlab = Matlab(matlab=config["MATLAB_LOCATION"])
path = os.getcwd() + "/"
mlab.start()
filename = config["f1arg1"]
start = int(config["f1arg2"])
end = int(config["f1arg3"])
res = mlab.run_func(path + config["f1"], {"arg1": filename, "arg2": start, "arg3": end})
mlab.stop()
print("done")
class DataPreprocess:
def __init__(self):
file = xlrd.open_workbook('excel/0.xls')
table = file.sheets()[sheet] #通过索引顺序获取工作表
nrows = table.nrows #行数
self.mlab = Matlab()
self.mlab.start()
# 该文件无用,只是为了避免一个路径引起的bug
res = self.mlab.run_func(
'C:/Users/ovewa/Desktop/git-storage/OS-ELM-matlab/test.m', 1)
self.ditu = []
for i in range(1, nrows):
self.ditu.append(table.row_values(i)[1:])
# 生成中间数据
def get_median(self):
for j in range(0, 20):
file = xlrd.open_workbook('excel/' + str(j) + '.xls')
table = file.sheets()[sheet] #通过索引顺序获取工作表
nrows = table.nrows #行数
data = []
for i in range(1, nrows):
data.append(table.row_values(i)[1:])
if os.path.isdir('middata' + str(sheet) + '/'):
pass
else:
os.mkdir('middata' + str(sheet) + '/')
with open('middata' + str(sheet) + '/' + str(j) + '.txt',
'wt') as f:
for y in range(len(data)):
for x in range(len(data[0])):
if int(data[y][x]) != 0 and int(self.ditu[y][x]) != 0:
f.write(
str(data[y][x] / self.ditu[y][x] - 1) + " " +
str(x) + " " + str(y) + "\n")
def get_none(self):
with open('middata' + str(sheet) + '/none.txt', 'wt') as f:
for y in range(13):
for x in range(10):
f.write(str(0) + " " + str(x) + " " + str(y) + "\n")
# 训练过程
# 可优化
# 1是训练条件不同结果不同
# 2是随机参数不同结果不同
# 多次训练取最优解
def training(self, model_num):
# 初始训练
res = self.mlab.run_func('OSELM_initial_training.m',
'middata' + str(sheet) + '/0.txt',
10,
'sin',
nargout=5)
IW = res['result'][0]
Bias = res['result'][1]
M = res['result'][2]
beta = res['result'][3]
# 增量学习
# ####!!!添加将 每一次增量学习结果的误差输出出来并可视化
for x in range(1, model_num + 1):
res = self.mlab.run_func('OSELM_increase_study.m',
'middata' + str(sheet) + '/' + str(x) +
'.txt',
IW,
Bias,
M,
beta,
'sin',
1,
nargout=4)
IW = res['result'][0]
Bias = res['result'][1]
M = res['result'][2]
beta = res['result'][3]
# 获取完整指纹库
res = self.mlab.run_func('OSELM_test_value.m',
'middata' + str(sheet) + '/none.txt', IW,
Bias, beta, 'sin')
result = res['result']
y = 0
data = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
for x in range(len(result)):
###重点,现在直接将初次训练结果保存,没有进行比较分析,待完善
data[x % 10][y] = int(self.ditu[y][x % 10] * (result[x] + 1))
if (x + 1) % 10 == 0:
y = y + 1
return data
def mlab_stop(self):
self.mlab.stop()
import pymatbridge
from pymatbridge import Matlab
# Initialise MATLAB
mlab = Matlab()
# Start the server
mlab.start()
# Run a test function: just adds 1 to the argument a
res = []
for i in range(5):
res.append(mlab.run_func('demo_func.m', {'a': i})['result'])
print res[-1]
# test the JSON parsing.
# quotes, and \n
res.append(mlab.run_code('fprintf(1,char([34,104,105,34,10]));'))
print res[-1]
# \b, \n
res.append(mlab.run_code('fprintf(1,char([8,10]));'))
print res[-1]
# \f, \n
res.append(mlab.run_code('fprintf(1,char([12,10]));'))
print res[-1]
# \r, \n
res.append(mlab.run_code('fprintf(1,char([13,10]));'))
print res[-1]
# \t, \n
res.append(mlab.run_code('fprintf(1,char([9,10]));'))
print res[-1]
# \\, \n
# Parameters Setting
filename = "./Input_data/example_lena.png"
npixs = 512
Nstd = 0.4
NE = 20
seedNo = 1
numImf = 6
runCEEMD = 1
maxSift = 10
typeSpline = 2
toModifyBC = 1
randType = 2
checksignal = 1
##
mlab = Matlab()
mlab.start()
mlab.run_code("addpaths")
# Fast 2dEEMD
res = mlab.run_func(
"meemd", filename, npixs, Nstd, NE, numImf, runCEEMD, maxSift, typeSpline, toModifyBC, randType, seedNo, checksignal
)
imfs = res["result"]
# Plot Results
HHTplots.example_lena(filename, imfs)
mlab.stop()
for i in range(0,len(second_level)):
name = second_level[i].split("Database/",1)[1].rsplit(".",1)[0]
address, name2 = os.path.split(name)
if name2 != ".DS_Store" and name2 != '':
if not os.path.exists('Database_5pt/'+address):
os.makedirs('Database_5pt/'+address)
pt = open("Database_5pt/"+address+'/'+name2+".5pt","w+")
img=io.imread(second_level[i])
dets = detector(img, 1)
for k, d in enumerate(dets):
shape = predictor(img, d)
left_eye = shape.part(45)
right_eye = shape.part(36)
pt.write(str(left_eye.x)+' '+str(left_eye.y)+'\n')
pt.write(str(right_eye.x)+' '+str(right_eye.y)+'\n')
nose = shape.part(30)
pt.write(str(nose.x)+' '+str(nose.y)+'\n')
left_mouse = shape.part(54)
right_mouse = shape.part(48)
pt.write(str(left_mouse.x)+' '+str(left_mouse.y)+'\n')
pt.write(str(right_mouse.x)+' '+str(right_mouse.y)+'\n')
pt.close()
ec_y = shape.part(36).y-shape.part(45).y
ec_y= (ec_y,-ec_y)(ec_y<0)
mc_y = shape.part(54).y-shape.part(48).y
mc_y =(mc_y,-mc.y)[ec_y<0]
ec_mc_y = mc_y-ec_y
pt.close()
#computations
mlab.run_func('src/software/face_db_align.m', {'face_dir': 'Database', 'ffp_dir': 'Database_5pt', 'ec_mc_y': ec_mc_y, 'ec_y': ec_y, 'img_size': size, 'save_dir': 'Database_preprocessed'})
def custom_filter(source, destination):
mlab = Matlab(matlab='/usr/local/MATLAB/R2013a/bin/matlab')
mlab.start()
res = mlab.run_func('./lineDect.m', {'arg1': source, 'arg2':destination})
mlab.start()
N = len(samples)
involved = [[] for _ in range(N)]
row = 0
for i in range(N):
for j in range(i + 1, N):
involved[i].append(row)
involved[j].append(row)
row += 1
rows = set(range(row))
notinvolved = [list(rows.difference(_)) for _ in involved]
dsts = np.zeros((len(samples), 2))
thetas = np.zeros((len(samples), 31))
i = 0
for train_row, test_row in zip(notinvolved, involved):
res = mlab.run_func('optimize_weights.m', {'A': A[train_row, :].tolist()})
x = np.array(res['result'])
dsts[i, :] = [
np.linalg.norm(np.dot(A[test_row, :], x)),
np.linalg.norm(np.dot(A[test_row, :], theta))
]
thetas[i, :] = x.ravel()
i += 1
s = clock()
res = mlab.run_func('optimize_weights.m', {
'A': A.tolist(),
'involved': involved,
'notinvolved': notinvolved
})
clock() - s
import pymatbridge
from pymatbridge import Matlab
# Initialise MATLAB
mlab = Matlab()
# Start the server
mlab.start()
# Run a test function: just adds 1 to the argument a
res = []
for i in range(5):
res.append(mlab.run_func('demo_func.m', {'a': i})['result'])
print res[-1]
# test the JSON parsing.
# quotes, and \n
res.append(mlab.run_code('fprintf(1,char([34,104,105,34,10]));'))
print res[-1]
# \b, \n
res.append(mlab.run_code('fprintf(1,char([8,10]));'))
print res[-1]
# \f, \n
res.append(mlab.run_code('fprintf(1,char([12,10]));'))
print res[-1]
# \r, \n
res.append(mlab.run_code('fprintf(1,char([13,10]));'))
print res[-1]
# \t, \n
res.append(mlab.run_code('fprintf(1,char([9,10]));'))
print res[-1]
# \\, \n
from pymatbridge import Matlab
import os
mlab = Matlab()
mlab.start()
dir_path = os.path.dirname(os.path.realpath(__file__))
dir_list = ['/Input_data', '/HHT_MATLAB_package', '/HHT_MATLAB_package/EMD',
'/HHT_MATLAB_package/checkIMFs', '/HHT_MATLAB_package/HT']
for d in dir_list:
res = mlab.run_func('addpath', dir_path + d)
class EMD():
def __init__(self, signal, Nstd, NE, num_imf=10, run_CEEMD=1, max_sift=10,
type_spline=2, modify_BC=1, rand_type=2, seed_no=1,
check_signal=1):
res = mlab.run_func('feemd_post_pro', signal, Nstd, NE, num_imf,
run_CEEMD, max_sift, type_spline, modify_BC,
rand_type, seed_no, check_signal)
self.imfs = res['result']
def get_oi(self):
oi = mlab.run_func('ratio1', self.imfs)
oi_pair = mlab.run_func('ratioa', self.imfs)
oi_dic = {'Non-orthogonal leakage of components': oi['result'],
'Non-orthogonal leakage for pair of adjoining components': oi_pair['result']}
return oi_dic
img = io.imread(second_level[i])
dets = detector(img, 1)
for k, d in enumerate(dets):
shape = predictor(img, d)
left_eye = shape.part(45)
right_eye = shape.part(36)
pt.write(str(left_eye.x) + ' ' + str(left_eye.y) + '\n')
pt.write(str(right_eye.x) + ' ' + str(right_eye.y) + '\n')
nose = shape.part(30)
pt.write(str(nose.x) + ' ' + str(nose.y) + '\n')
left_mouse = shape.part(54)
right_mouse = shape.part(48)
pt.write(str(left_mouse.x) + ' ' + str(left_mouse.y) + '\n')
pt.write(str(right_mouse.x) + ' ' + str(right_mouse.y) + '\n')
pt.close()
ec_y = shape.part(36).y - shape.part(45).y
ec_y = (ec_y, -ec_y)(ec_y < 0)
mc_y = shape.part(54).y - shape.part(48).y
mc_y = (mc_y, -mc.y)[ec_y < 0]
ec_mc_y = mc_y - ec_y
pt.close()
#computations
mlab.run_func(
'src/software/face_db_align.m', {
'face_dir': 'Database',
'ffp_dir': 'Database_5pt',
'ec_mc_y': ec_mc_y,
'ec_y': ec_y,
'img_size': size,
'save_dir': 'Database_preprocessed'
})
randType = 2
checksignal = 1
##
data = np.loadtxt(filename)
time = data[:, 0]
amp = data[:, 1]
dt = time[1] - time[0]
##
mlab = Matlab()
mlab.start()
mlab.run_code('addpaths')
# Fast EEMD
res = mlab.run_func('feemd_post_pro', amp, Nstd, NE, numImf, runCEEMD, maxSift,
typeSpline, toModifyBC, randType, seedNo, checksignal)
imfs = res['result']
# Orthogonality Checking
oi = mlab.run_func('ratio1', imfs)
oi_pair = mlab.run_func('ratioa', imfs)
print('Non-orthogonal leakage of components:')
print(oi['result'])
print('Non-orthogonal leakage for pair of adjoining components:')
print(oi_pair['result'])
# Hilbert Transform
fa_res = mlab.run_func('fa', imfs[:, 3], dt, 'hilbtm', 'pchip', 0, nargout=2)
fa = fa_res['result']
ifreq = np.transpose(fa[0])
mlab.start()
N = len(samples)
involved = [[] for _ in range(N)]
row = 0
for i in range(N):
for j in range(i+1, N):
involved[i].append(row)
involved[j].append(row)
row += 1
rows = set(range(row))
notinvolved = [list(rows.difference(_)) for _ in involved]
dsts = np.zeros((len(samples), 2))
thetas = np.zeros((len(samples), 31))
i = 0
for train_row, test_row in zip(notinvolved, involved):
res = mlab.run_func('optimize_weights.m', {'A': A[train_row, :].tolist()})
x = np.array(res['result'])
dsts[i, :] = [np.linalg.norm(np.dot(A[test_row, :], x)),
np.linalg.norm(np.dot(A[test_row, :], theta))]
thetas[i, :] = x.ravel()
i += 1
s = clock()
res = mlab.run_func('optimize_weights.m',
{'A': A.tolist(), 'involved': involved,
'notinvolved': notinvolved})
clock() - s
mlab.stop()
dsts = np.array(res['result']['distances'])
thetas = np.array(res['result']['thetas'])
from sklearn.metrics import average_precision_score
import matplotlib.pyplot as plt
with open("exp.txt", "r") as inf:
inf.readline()
int_array = [line.strip("\n").split()[:] for line in inf]
predictR = np.array(int_array, dtype=np.float64)
with open("exp1.txt", "r") as inf1:
inf1.readline()
int_array1 = [line.strip("\n").split()[:] for line in inf1]
final_lable = np.array(int_array1, dtype=np.float64)
mlab = Matlab()
mlab.start()
# print os.getcwd()
# self.predictR = mlab.run_func(os.sep.join([os.getcwd(), "kbmf2k", "kbmf.m"]), {'Kx': drugMat, 'Kz': targetMat, 'Y': R, 'R': self.num_factors})['result']
res = mlab.run_func(os.path.realpath(os.sep.join(["..\pudt", "AUC.m"])), {
'test_targets': final_lable,
'output': predictR
})
predictAUC = res['result']
# print os.path.realpath(os.sep.join(['../kbmf2k', "kbmf.m"]))
mlab.stop()
# score = self.predictR[test_data[:, 0], test_data[:, 1]]
score = predictAUC
fpr, tpr, thr = roc_curve(final_lable, np.array(predictR))
auc_val = auc(fpr, tpr)
print("predictAUC: " + str(predictAUC) + ", AUC: " + str(auc_val))
class FingerprintUpdate:
def __init__(self):
self.mlab = Matlab()
self.mlab.start()
# 该文件无用,只是为了避免一个路径引起的bug
res = self.mlab.run_func(
'C:/Users/ovewa/Desktop/git-storage/OS-ELM-matlab/test.m', 1)
# 生成中间数据
def get_median(self, ditu, data, model_num, ap_num):
if os.path.isdir('middata' + str(ap_num) + '/'):
pass
else:
os.mkdir('middata' + str(ap_num) + '/')
with open('middata' + str(ap_num) + '/' + str(model_num) + '.txt',
'wt') as f:
for x in range(len(data)):
for y in range(len(data[0])):
if int(data[x][y]) != 0 and int(ditu[x][y]) != 0:
f.write(
str(data[x][y] / ditu[x][y] - 1) + " " + str(x) +
" " + str(y) + "\n")
def get_none(self, ditu, ap_num):
if os.path.isdir('middata' + str(ap_num) + '/'):
pass
else:
os.mkdir('middata' + str(ap_num) + '/')
with open('middata' + str(ap_num) + '/none.txt', 'wt') as f:
for x in range(len(ditu)):
for y in range(len(ditu[0])):
f.write(str(0) + " " + str(x) + " " + str(y) + "\n")
# 训练过程
# 可优化
# 1是训练条件不同结果不同
# 2是随机参数不同结果不同
# 多次训练取最优解
def training(self, model_num, ditu, ap_num):
# 初始训练
res = self.mlab.run_func('OSELM_initial_training.m',
'middata' + str(ap_num) + '/1.txt',
10,
'sin',
nargout=5)
IW = res['result'][0]
Bias = res['result'][1]
M = res['result'][2]
beta = res['result'][3]
# 增量学习
# ####!!!添加将每一次增量学习结果的误差输出出来并可视化
for x in range(2, (model_num + 1)):
res = self.mlab.run_func('OSELM_increase_study.m',
'middata' + str(ap_num) + '/' + str(x) +
'.txt',
IW,
Bias,
M,
beta,
'sin',
1,
nargout=4)
IW = res['result'][0]
Bias = res['result'][1]
M = res['result'][2]
beta = res['result'][3]
# 获取完整指纹库
res = self.mlab.run_func('OSELM_test_value.m',
'middata' + str(ap_num) + '/none.txt', IW,
Bias, beta, 'sin')
result = res['result']
y = 0
data = []
for i in range(len(ditu)):
data.append([])
for j in range(len(ditu[0])):
data[i].append(-1)
for x in range(len(result)):
###重点,现在直接将初次训练结果保存,没有进行比较分析,待完善
data[x % 10][y] = int(ditu[x % 10][y] * (result[x] + 1))
if (x + 1) % 10 == 0:
y = y + 1
return data
def mlab_stop(self, ap_mac):
self.mlab.stop()
# 删除中间文件
for x in range(len(ap_mac)):
if os.path.isdir('middata' + str(x) + '/'):
shutil.rmtree('middata' + str(x) + '/')
