def ReadDiskData(self, filename):
if self.verbose:
print 'Reading from disk %s' % filename
ext = os.path.splitext(filename)[1]
if ext == '.npy':
data = np.load(filename)
elif ext == '.mat':
if 'key' in kwargs.keys():
key = kwargs['key']
else:
key = desc
data = scipy.io.loadmat(filename, struct_as_record = True)[key]
elif ext == '.p':
data = pickle.load(gzip.GzipFile(filename, 'rb'))
elif ext == '.txt':
data = np.loadtext(filename)
elif ext == '.npz':
data = self.load_sparse(filename)
else:
raise Exception('Unknown file extension %s' % ext)
if data.dtype == 'float64':
data = data.astype('float32')
if self.subtract_mean:
data -= self.mean
if self.divide_stddev:
data /= self.stddev
if len(data.shape) == 1 or (len(data.shape)==2 and (
data.shape[0] == 1 or data.shape[1] == 1)):
data = data.reshape(-1, 1)
return data
def ReadDiskData(self, filename, key=''):
"""Reads data from filename."""
if self.verbose:
print 'Reading from disk %s' % filename
ext = os.path.splitext(filename)[1]
if ext == '.npy':
data = np.load(filename)
elif ext == '.mat':
data = scipy.io.loadmat(filename, struct_as_record = True)[key]
elif ext == '.p':
data = pickle.load(gzip.GzipFile(filename, 'rb'))
elif ext == '.txt':
data = np.loadtext(filename)
elif ext == '.npz':
data = Disk.LoadSparse(filename, verbose=self.verbose)
elif ext == '.spec':
data = Disk.LoadPickle(filename, key, verbose=self.verbose)
else:
raise Exception('Unknown file extension %s' % ext)
if data.dtype == 'float64':
data = data.astype('float32')
# 1-D data as column vector.
if len(data.shape) == 1 or (len(data.shape)==2 and data.shape[0] == 1):
data = data.reshape(-1, 1)
return data
def ReadDiskData(self, filename, key=''):
"""Reads data from filename."""
if self.verbose:
print 'Reading from disk %s' % filename
ext = os.path.splitext(filename)[1]
if ext == '.npy':
data = np.load(filename)
elif ext == '.mat':
data = scipy.io.loadmat(filename, struct_as_record=True)[key]
elif ext == '.p':
data = pickle.load(gzip.GzipFile(filename, 'rb'))
elif ext == '.txt':
data = np.loadtext(filename)
elif ext == '.npz':
data = Disk.LoadSparse(filename, verbose=self.verbose)
elif ext == '.spec':
data = Disk.LoadPickle(filename, key, verbose=self.verbose)
else:
raise Exception('Unknown file extension %s' % ext)
if data.dtype == 'float64':
data = data.astype('float32')
# 1-D data as column vector.
if len(data.shape) == 1 or (len(data.shape) == 2
and data.shape[0] == 1):
data = data.reshape(-1, 1)
return data
def read_feature_from_file(filename):
'''
读取特征值属性，然后将其以矩阵形式返回
'''
f = np.loadtext(filename)
#返回特征位置、描述子
return f[:, :4], f[:, 4:]
def graphData(stock):
try:
stockFile = stock+'.txt'
date, closep,highp,lowp,openp,volume = np.loadtext(stockFile,delimeter=',',unpack=True,converters ={ 0: mdates.strpdate2num('%Y%m%d')})
fig = plt.figure()
ax1 = plot.subplot(1,1,1)#(2,3,1) would be position 1 in a 2 by 3 square
ax1.plot(date, openp)
ax1.plot(date, highp)
ax1.plot(date, lowp)
ax1.plot(date, closep)
ax1.xaxis.set_major+locator(mticker.MaxNLocator(10)) #max of 10 dates
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
for label in ax1.xaxis.get_ticklables():
label.set_rotation(45)
plt.show()
except Exception,e:
print 'failed main loop',str(e)
def prepare(self, resfile=None):
"""prepare system to scan for eta parameters by reading in resonance
locations
Args:
resfile (str): optional path resonance locations
If none supplied, then most recent _resloc file is used
Outputs:
freqs (list): list of frequency locations
"""
if resfile is not None:
reslocs = resfile
else:
resfiles = sorted([f for f in os.listdir(self.outputdir) if
"res_locs" in f]) # move nickname to a variable?
reslocs = resfiles[-1] # grab the most recent
data = np.loadtext(reslocs, dtype=float, delimiter=',', skiprows=1)
freqs = data[:,0] # assumes frequencies are in the first column
self.freqs = freqs
return freqs
from qe import quaternion_to_euler as qte
from qe import euler_to_quaternion as etq
import sys
import numpy as np
if __name__=="__main__":
if(len(sys.argv)<3):
print("Enter infile,outfile and choice")
exit(1)
infile_n=sys.argv[1]
outfile_n=sys.argv[2]
choice=sys.argv[3]
infile=open(infile_n,"r")
outfile=open(outfile_n,"w")
i=np.loadtext(infile,delimiter=",")
if(choice=='0'):
if(i.shape[1]==4):
euler=qte(i)
np.writetext(outfile,delimiter=",")
else:
print("Wrong dimensions")
exit(1)
else:
if(i.shape[1]==3):
quaternion=etq(i)
np.writetext(outfile,delimiter=",")
else:
print("Wrong dimensions")
exit(1)
def testSampleData(self):
# Tests that the np array generated by the data_analysis function matches saved expected results
csv_data = pd.read_csv(fname=SAMPLE_DATA_FILE_LOC)
analysis_results = data_processing(csv_data)
expected_results = np.loadtext(fname=os.path.join(TEST_DATA_DIR, "expected_data.csv"), delimiter=',')
self.assertTrue((expected_results == analysis_results).all())
import numpy as np
from matplotlib import plot as plt
x = np.loadtext("random.dat")
plt.hist(x, bins=100)
plt.show()
import numpy
filename = 'indian-diabities.data.csv'
raw_data = open(filename, 'r')
data = numpy.loadtext(raw_data, delimiter = ',')
print("numpy loadtext : ", data.shape)
raw_data.close()
##########################################################################################
# Settings
##########################################################################################
# Number of neurons in each layer.
#
N1 = 28 ** 2
N2 = 500
N3 = 500
N4 = 500
N5 = 10
# Load neural network parameters.
#
b1 = np.loadtext("bin/train_b1.csv")
W12 = np.loadtext("bin/train_W12.csv")
b2 = np.loadtext("bin/train_b2.csv")
W23 = np.loadtext("bin/train_W23.csv")
b3 = np.loadtext("bin/train_b3.csv")
W34 = np.loadtext("bin/train_W34.csv")
b4 = np.loadtext("bin/train_b4.csv")
W45 = np.loadtext("bin/train_W45.csv")
b5 = np.loadtext("bin/train_b5.csv")
##########################################################################################
# Methods
##########################################################################################
# Activation functions.
#
import numpy as np
data = np.loadtext(
fname = 'data/inflammation-01.csv'
delimiter = ','
)
min_inflammation = np.min(
data,
axis = 0
)
plt.plot(min_inflammation)
elif (valIMC < 17):
return str(valIMC) + ': Magreza moderada'
elif (valIMC < 18.5):
return str(valIMC) + ': Magreza leve'
elif (valIMC < 25):
return str(valIMC) + ': Saudavel'
elif (valIMC < 30):
return str(valIMC) + ': Sobrepeso'
elif (valIMC < 35):
return str(valIMC) + ': Obesidade Grau I'
elif (valIMC < 40):
return str(valIMC) + ': Obesidade Grau II (Severa)'
else:
return str(valIMC) + ': Obesidade Grau III (Morbida)'
# Os parametros sao:
# Local do arquivo
# Delimitador usado la no gerador
# Se houver mais de uma variavel para devemos "desempacotalas", e ha, entao True
# Tipo de dado
localArquivo = r'%s' % os.getcwd().replace('\\', '/') + '/peso.csv'
altura, peso, forca = np.loadtext(localArquivo + '/peso.csv',
delimiter=';',
unpack=True,
dtype='float')
# Se pedirmos para printar, observer que saira tudo organizado
print(altura)
""" """ # Importing the KERAS Sequential model. from keras.models import Sequentialfrom from keras.layers import Dense import numpy # Initailizing a seed value to an integer... I wonder why they choose 7 seed = 7 numpy.random.seed(seed); # Loading the data set - they are using the PIMA Diabetes dataset... Do I Have access to this dataset? dataset = numpy.loadtext(); # we'll need to handle this in some way. # loading the input values to x and label values y using slicing... x = datasett[:,0:8] # probably some kind of local definition. y = dataset][:, 8] # see above. # Initializing the Sequential model from KERASmodel = Sequential() # creatin a 16 neuron hidden layer with Linear Rectified activation function... pretty cool model.add(Dense(16, input_dim=8, int='uniform', activation='relu')) # creating an 8 neuron hidden layer model.add(Dense(8, init='uniform',activation='relu')) # adding an output layer.
cost_val, _ = sess.run([cost, train], feed_dict={X: x_data, Y: y_data})
if step % 200 == 0:
print(step, cost_val) # Finish Training
# Accuracy report
h, c, a = sess.run([hypothesis, predicted, accuracy],
feed_dict={
X: x_data,
Y: y_data
})
print("\nHypothesis: ", h, "\nCorrect (Y): ", c, "\nAccuracy: ", a)
# ## Classifying diabetes 당뇨병
# : csv 파일을 불러오고 학습 후에 예측해보자
import numpy as np
xy = np.loadtext('data~ .csv', delimiter=',', dtype=np.float32)
# , 로 seperate하고 data type도 지정해줌
# Slicing - list에서 원하는 범위만큼 get
x_data = xy[:, 0:-1]
y_data = xy[:, [-1]]
# placeholder shape check하고 나머지 방법은 동일
X = tf.placeholder(tf.float32, shape=[None, 8])
Y = tf.placeholder(tf.float32, shape=[None, 1])
W = tf.Variable(tf.random_normal([8, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
def get_data(phosphors='./test_calib/spectrum_after.dat',
fundamentals='./lms_cone_fundamentals.csv'):
phosphors = np.loadtext()
def readSE(self, output_file):
'''
Read in the given output file
'''
new_catalog = np.loadtext(output_file)
def readSE(self,output_file): ''' Read in the given output file ''' new_catalog = np.loadtext(output_file)
#Load data
filenamevariable = 'filename.txt'
file = open(filenamevariable, mode = 'r')
#'r' is to read the file
text = file.read()
file.close()
print(text)
#or to avoid having to close the file
open('filename.txt', 'r') as file:
print(file.read())
#Without a header.
import numpy as np
filenamevariable = 'filename.txt'
data = np.loadtext(filenamevariable, delimiter = ',')
#With a header
import numpy as np
filenamevariable = 'filename.txt'
data = np.load.txt(filenamevariable, delimiter = ',', skiprows =1)
#To load specific columns
import numpy as np
filenamevariable = 'filename.txt'
data = np.loadtxt(filenamevariable, delimiter = ',', skiprows = 1, usecols=[0,2])
#To load columns with specific data types
import numpy as np
filenamevariable = 'filename.txt'
data = np.loadtxt(filenamevariable, delimiter = ',', dtype = str)
