def convolute(self, kernel, fourier=None):
"""
The implement of convolution.
It will choose a better algorithm to do it depends on the size of kernel and image.
:param kernel:
:param fourier:Whether use fourier transform to calculate the convolution
:return:
"""
if not isinstance(kernel, Kernel.Kernel):
try:
kernel = Kernel.Kernel(kernel)
except Exception:
raise Exception('The input must be a kernel')
kernel_size = kernel.shape[0] * kernel.shape[1]
image_size = np.sqrt(self.shape[0] * self.shape[1])
if fourier is not None:
if fourier:
return self.__fourier_convolution(kernel)
else:
return self.__base_convolution(kernel)
# According to the Mark Nixon's book(p.87 3rd version),If m² < 4*log(N) + 1,
# then we should use direct implementation, otherwise the fourier transform should be considered.
else:
if kernel_size < 4 * np.log(image_size) + 1:
return self.__base_convolution(kernel)
else:
return self.__fourier_convolution(kernel)
def createcobs(self, clustertype="kmeans", stats=False):
'''
looks at all the files in self.directory and finds and with the same base name as self.name.
each file is turned into a cob object, and add to self.cobs
'''
for cobfile in os.listdir(self.directory):
if '_' + self.name + "." in cobfile:
filenamewithoutlastextension = os.path.splitext(cobfile)[0]
basename = os.path.splitext(filenamewithoutlastextension)[0]
kernellist = []
with open(self.directory + "/" + cobfile) as csvfile:
csvreader = csv.reader(csvfile)
csvlist = list(csvreader)
listofpixels = []
currentkernel = 1
for line in csvlist[:-1]:
try:
if line[0] == 'Image':
pass
elif int(line[1]
) != currentkernel and line[4] != '':
kernellist.append(
Kernel.Kernel(listofpixels,
name=currentkernel,
clustertype="dbscan",
stats=stats))
listofpixels = []
currentkernel = int(line[1])
currentpixel = [
int(line[2]),
int(line[3]),
int(line[4])
]
listofpixels.append(currentpixel)
elif int(line[1]) == currentkernel:
currentpixel = [
int(line[2]),
int(line[3]),
int(line[4])
]
listofpixels.append(currentpixel)
except Exception, e:
print str(e), "in create cobs"
IndexError
currentcob = Cob.Cob(kernellist,
basename,
pixelcluster=False,
clustertype=clustertype,
stats=stats)
print "Finished Cob: ", basename
self.coblist.append(currentcob)
def setKernelsandRGB(self, show=False):
'''
For each entry in self.cobs, it will open the file with that name in repDirectory.
The file is a csv file of the format "accessionName, Kernel, R, G, B". This function will return a list of
ints that correspond to the number of kernels that are associated with the Cob file.
'''
print "Creating Kernels and Pixels"
progressBar = 0
for key in self.cobs:
progressBar += 1
kernelList = []
filePath = self.repDirectory + "/" + key + ".tif.csv"
with open(filePath) as csvFile:
csvReader = csv.reader(csvFile)
csvList = list(csvReader)
print "Cob: ", progressBar, "/", len(self.cobs)
listofpixels = []
currentKernel = 1
for line in csvList:
try:
if line[0] == 'Image':
pass
elif int(line[1]) != currentKernel and line[4] != '':
kernelList.append(
Kernel.Kernel(listofpixels,
name=currentKernel))
if show == True:
print "Kernel: %s" % currentKernel
listofpixels = []
currentKernel = int(line[1])
currentPixel = Pixel.Pixel(int(line[2]),
int(line[3]),
int(line[4]))
listofpixels.append(currentPixel)
elif int(line[1]) == currentKernel:
currentPixel = Pixel.Pixel(int(line[2]),
int(line[3]),
int(line[4]))
listofpixels.append(currentPixel)
except:
IndexError
self.cobs[key] = Cob.Cob(kernelList)
print "Kernel's Initialized for %s" % key
print "All Cob's Initialized"
space = []
count = 0
for i in range(100):
space.append([])
for j in range(100):
if rr(100) < 20:
space[i].append(1)
else:
space[i].append(0)
patterns = [[[0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [1, 1, 1],
[0, 0, 0]],
[[0, 1, 0], [0, 1, 0], [0, 1, 0]]]
system = Kernel(3, 3, patterns)
system['x'] = 20
system['y'] = 20
mover = Mover()
target = [50, 40]
for i in range(1000):
input = extract(space, system)
system.compute(target + [input])
mover.compute([system['output']])
target = [system['x'], system['y']]
output = mover['output']
if output == 0:
target[1] = system['y'] - 1
Programa5.add(IoInstruccion1)
Programa5.add(IoInstruccion5)
Programa5.add(CpuInstruccion4)
myCDROM = CDROM()
myPrinter = Printer()
myMMU = MMU()
myDiskWithPrograms = Disk()
myDiskWithPrograms.load(Programa1)
myDiskWithPrograms.load(Programa2)
myDiskWithPrograms.load(Programa3)
myDiskWithPrograms.load(Programa4)
myDiskWithPrograms.load(Programa5)
myEmptyDisk = Disk()
asignacionContinua = AsignacionContinua(PrimerAjuste(),myMMU)
myMMU.setLogicalMemory(asignacionContinua)
#myKernel = Kernel(Fifo, myMMU, myEmptyDisk)
myKernel = Kernel(Fifo, myMMU, myDiskWithPrograms)
myKernel.addDevice(myCDROM)
myKernel.addDevice(myPrinter)
myKernel.initializeThread()
myKernel.runProcess("Programa1")
myKernel.runProcess("Programa2")
myKernel.runProcess("Programa3")
myKernel.runProcess("Programa4")
myKernel.runProcess("Programa5")
import ParseGraph
try:
import xml.etree.cElementTree as ET
except ImportError:
import cElementTree as ET
import sys
import gzip
from numpy import *
from numpy.linalg import *
import Kernel
from MatrixBuilders import *
floattype = float64
parser = "split_parse"
tokenizer = "split"
kernel = Kernel.Kernel()
feature_counter = 0
def readInstances(a_file):
"""Parses the analysis file to extract the analyses on which the
graphs will be based on"""
result = {}
print >> sys.stderr, "\rParsing file '%s' " %(a_file),
acontent = iter(ET.iterparse(a_file,events=("start","end")))
event, root = acontent.next()
for event, elem in [(a,b) for a,b in acontent if a=="end" and b.tag=="document"]:
doc_id = elem.get("id")
result[doc_id] = elem
return result
# import sys
# sys.path.append("../")
import Kernel
Seyana = Kernel.Kernel()
Seyana.learn("other.aiml")
Seyana.learn("wxAndroid-life.aiml")
def dialogue(Content):
response = Seyana.respond(Content)
if response == "":
response = "对不起，我没明白您在说什么"
return response
# -*- coding: utf-8 -*- ''' @author: [email protected] @license: (C) Copyright 2017 @desc: python3 版本中文Alice，暂时简单添加空格 @DateTime: Created on 2017/11/15，at 10:20 ''' import Kernel import opencc alice = Kernel.Kernel() alice.learn("cn-test.aiml") while True: input_data = input('Alice请您提问...>>') response = alice.respond(input_data) response = response.replace(" ", "") print(response)
from plot_data import *
import Kernel
import train
if __name__ == '__main__':
# Work on multiple traces of the same subject
extract_data_files(1) # 1 - indicates that consider only the first subject
#print(train_file_names)
for file in train_file_names:
frames_list, x_coordinates_list = file_wise_extract_data(
file, 1, is_training=True)
test_frames_list, test_x_coordinates_list = file_wise_extract_data(
file, 1, is_training=False)
gp = None
kernel = Kernel.Kernel(1.0, 1.0)
for frames, coordinates in zip(frames_list, x_coordinates_list):
#plot_marker('1', np.reshape(frames, (-1, 1)), coordinates)
#plot_show()
if gp:
gp = train.train(gp, kernel, frames, coordinates, sigma2=1.0)
else:
gp = train.train(None, kernel, frames, coordinates, sigma2=1.0)
define_labels(file, '1')
plot_marker_test(np.reshape(test_frames_list, (-1, 1)),
np.reshape(test_x_coordinates_list, (-1, 1)),
None,
'bo',
labels='Observations')
for test_frames, test_coordinates in zip(test_frames_list,
#coding:utf-8
import Kernel
if __name__ == '__main__':
Kia = Kernel.Kernel()
#Kia.learn("cn-test.aiml")
#Kia.learn("bye.aiml")
#Kia.learn("Common conversation.aiml")
#Kia.learn("funny.aiml")
Kia.learn("Kia.aiml")
#Kia.learn("OrdinaryQuestion.aiml")
#Kia.learn("personname.aiml")
Kia.learn("nk.aiml")
#Kia.learn("tools.aiml")
#Kia.learn("tuling.aiml")
while True:
print('>>' + Kia.respond(input('问小开>>')))
def main(argv):
#path="C:\\Temp\\tmp_qqbot" #存放临时文件和aiml文件的路径
path=this_path+"\\temp"
isDebug=True
question='null'
msid='null'
try:
opts,args=getopt.getopt(argv,"hc:s:",["content=","sid="])
except getopt.GetoptError:
print('Error: Kia.py -c <content> -s <sid>')
print(' or: Kia.py --content=<content> --sid=<sid>')
sys.exit(2)
for opt,arg in opts:
if opt=="-h":
print('usage:Kia.py -c <content> -s <sid>')
print(' or: Kia.py --content=<content> --sid=<sid>')
sys.exit()
elif opt in ("-s","--sid"):
msid=arg
elif opt in ("-c","--content"):
question=arg
print('q='+question)
print('sid='+msid)
if (question=='null' or msid=='null'):
print('Error: Kia.py -c <content> -s <sid>')
print(' or: Kia.py --content=<content> --sid=<sid>')
sys.exit(2)
#——————————————学习初始化————————_
Kia=Kernel.Kernel()
Kia.learn(path+"\\Kia.aiml")
Kia.learn(path+"\\nk.aiml")
Kia.learn(path+"\\learnNewU.aiml")
os.chdir(path)
if isDebug :
print(os.getcwd())
print(os.listdir())
#QandSID=input('问小开>>').split(' ')
#question=QandSID[0]
#sid=QandSID[1]
#——————添加本次对话————————
caches=os.listdir()
cache=open(msid+'.txt','a+')
cache.write(question+'\n')
cache.close()
#----------复原对话----------
cache=open(msid+'.txt','r')
lines=cache.readlines()
res1=RunQA.major(question)
if (res1.find('#fail')>-1):
for line in lines:
respond=Kia.respond(line.strip()) #最后一次respond即为答案
if isDebug:
print('usr:'******'Kia:'+respond)
else:
respond=res1
print('ans='+respond.strip())
import Kernel
import Processo
import time
def varius(*arg):
st = "a "
for i in arg:
st = st + "%i "
print(st % arg)
varius(1, 2, 4, 5, 6)
kn = Kernel.Kernel(usuario, quantum, escalonador[0])
kn.start()
def recuperar(nome, identidade):
global kn
return kn.encontrar(nome, identidade)
##print(software["fibonacciN"])
##Processos com busy wait Peterson
kn.novoProcesso("mulB", 2, software["mulB"], 2.4, 20, "A")
kn.novoProcesso("mulB", 2, software["mulB"], 2.1, 21, "A")
kn.novoProcesso("adB", 2, software["adB"], 2.4, 20, "B")
def main(self):
memoria = Memory()
file_system = FileSystem()
kernel = Kernel(memoria, file_system)
kernel.execute()
try:
from math import tau
except ImportError:
from math import pi
tau = pi * 2
"""
Laser software for the Stock-LIHUIYU laserboard.
MeerK40t (pronounced MeerKat) is a built-from-the-ground-up MIT licensed
open-source laser cutting software. See https://github.com/meerk40t/meerk40t
for full details.
"""
kernel = Kernel()
# TODO: CLI Needs an option to change default speed, etc, parameters.
# TODO: CLI Needs home command / lock, unlock.
# TODO: CLI Needs command for load special module.
parser = argparse.ArgumentParser()
parser.add_argument('-l',
'--list',
type=str,
nargs="*",
help='list all device properties')
parser.add_argument('-z',
'--no_gui',
action='store_true',
help='run without gui')