time-series + classification
recurrence plot and CNN for time-series classification
It is helpful to think of this architecture as defining two sub-models: the CNN Model for feature extraction and the LSTM Model for interpreting the features across time steps.
time-series 데이터를 2차원 그림으로 전환한다.
이 때, recurrence plot을 활용한다. 자연스럽게 2차원 그림이 얻어진다.
그 다음 CNN방법을 활용한다. CNN 방법은 특히, 그림이 의미하는 바를 잘 이해하는 데 아주 유용한 방법이다.
recurrence plot정의를 넘어서 cross recurrence plot의 정의도 가능하다.
import numpy as np
from scipy.spatial.distance import pdist, squareform
def rec_plot(s, eps=0.1, steps=10):
d = pdist(s[:,None])
d = np.floor(d/eps)
d[d>steps] = steps
Z = squareform(d)
return Z
def recurrence_plot(s, eps=None, steps=None):
if eps==None: eps=0.1
if steps==None: steps=10
d = sk.metrics.pairwise.pairwise_distances(s)
d = np.floor(d / eps)
d[d > steps] = steps
#Z = squareform(d)
return d
model = Sequential()
model.add(Convolution2D(32, (3, 3), activation='relu', input_shape=(1,32,32), data_format='channels_first'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Convolution2D(32, (3, 3),activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
#model.add(LeakyReLU(alpha=0.03))
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
#reshape to include depth
X_train = x_train.reshape(x_train.shape[0], 1, 32,32)
#convert to float32 and normalize to [0,1]
X_train = X_train.astype('float32')
X_train /= np.amax(X_train)
# convert labels to class matrix, one-hot-encoding
Y_train = np_utils.to_categorical(y_train, 3)
# split in train and test set
X_train, x_test, Y_train, y_test = train_test_split(X_train, Y_train, test_size=0.1)
model.fit(X_train, Y_train, epochs=200, batch_size=16,shuffle=True)
predictions= model.predict(x_test)
rounded = [np.argmax(x) for x in predictions]
print(K.eval(metrics.categorical_accuracy(y_test, np_utils.to_categorical(rounded, 3))))
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# lstm autoencoder recreate sequence
from numpy import array
from keras.models import Sequential
from keras.models import Model
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import RepeatVector
from keras.layers import TimeDistributed
from keras.utils import plot_model
# define input sequence
sequence = array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
# reshape input into [samples, timesteps, features]
n_in = len(sequence)
sequence = sequence.reshape((1, n_in, 1))
# define model
model = Sequential()
model.add(LSTM(100, activation='relu', input_shape=(n_in,1)))
model.add(RepeatVector(n_in))
model.add(LSTM(100, activation='relu', return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(sequence, sequence, epochs=300, verbose=0)
# connect the encoder LSTM as the output layer
model = Model(inputs=model.inputs, outputs=model.layers[0].output)
plot_model(model, show_shapes=True, to_file='lstm_encoder.png')
# get the feature vector for the input sequence
yhat = model.predict(sequence)
print(yhat.shape)
print(yhat)
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model = Sequential()
model.add(LSTM(32, input_shape=(10, 2)))
model.add(Dense(1))
---------------------------------------------------------------------------------------------------------------------
from numpy import array
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import TimeDistributed
from keras.layers import LSTM
# prepare sequence
length = 5
seq = array([i/float(length) for i in range(length)])
X = seq.reshape(1, length, 1)
y = seq.reshape(1, length, 1)
# define LSTM configuration
n_neurons = length
n_batch = 1
n_epoch = 1000
# create LSTM
model = Sequential()
model.add(LSTM(n_neurons, input_shape=(length, 1), return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.compile(loss='mean_squared_error', optimizer='adam')
print(model.summary())
# train LSTM
model.fit(X, y, epochs=n_epoch, batch_size=n_batch, verbose=2)
# evaluate
result = model.predict(X, batch_size=n_batch, verbose=0)
for value in result[0,:,0]:
print('%.1f' % value)
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# lstm model
from numpy import mean
from numpy import std
from numpy import dstack
from pandas import read_csv
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers import LSTM
from keras.utils import to_categorical
from matplotlib import pyplot
# load a single file as a numpy array
def load_file(filepath):
dataframe = read_csv(filepath, header=None, delim_whitespace=True)
return dataframe.values
# load a list of files and return as a 3d numpy array
def load_group(filenames, prefix=''):
loaded = list()
for name in filenames:
data = load_file(prefix + name)
loaded.append(data)
# stack group so that features are the 3rd dimension
loaded = dstack(loaded)
return loaded
# load a dataset group, such as train or test
def load_dataset_group(group, prefix=''):
filepath = prefix + group + '/Inertial Signals/'
# load all 9 files as a single array
filenames = list()
# total acceleration
filenames += ['total_acc_x_'+group+'.txt', 'total_acc_y_'+group+'.txt', 'total_acc_z_'+group+'.txt']
# body acceleration
filenames += ['body_acc_x_'+group+'.txt', 'body_acc_y_'+group+'.txt', 'body_acc_z_'+group+'.txt']
# body gyroscope
filenames += ['body_gyro_x_'+group+'.txt', 'body_gyro_y_'+group+'.txt', 'body_gyro_z_'+group+'.txt']
# load input data
X = load_group(filenames, filepath)
# load class output
y = load_file(prefix + group + '/y_'+group+'.txt')
return X, y
# load the dataset, returns train and test X and y elements
def load_dataset(prefix=''):
# load all train
trainX, trainy = load_dataset_group('train', prefix + 'HARDataset/')
print(trainX.shape, trainy.shape)
# load all test
testX, testy = load_dataset_group('test', prefix + 'HARDataset/')
print(testX.shape, testy.shape)
# zero-offset class values
trainy = trainy - 1
testy = testy - 1
# one hot encode y
trainy = to_categorical(trainy)
testy = to_categorical(testy)
print(trainX.shape, trainy.shape, testX.shape, testy.shape)
return trainX, trainy, testX, testy
# fit and evaluate a model
def evaluate_model(trainX, trainy, testX, testy):
verbose, epochs, batch_size = 0, 15, 64
n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
model = Sequential()
model.add(LSTM(100, input_shape=(n_timesteps,n_features)))
model.add(Dropout(0.5))
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit network
model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
# evaluate model
_, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
return accuracy
# summarize scores
def summarize_results(scores):
print(scores)
m, s = mean(scores), std(scores)
print('Accuracy: %.3f%% (+/-%.3f)' % (m, s))
# run an experiment
def run_experiment(repeats=10):
# load data
trainX, trainy, testX, testy = load_dataset()
# repeat experiment
scores = list()
for r in range(repeats):
score = evaluate_model(trainX, trainy, testX, testy)
score = score * 100.0
print('>#%d: %.3f' % (r+1, score))
scores.append(score)
# summarize results
summarize_results(scores)
# run the experiment
run_experiment()
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# cnn lstm model
from numpy import mean
from numpy import std
from numpy import dstack
from pandas import read_csv
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers import LSTM
from keras.layers import TimeDistributed
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.utils import to_categorical
from matplotlib import pyplot
# load a single file as a numpy array
def load_file(filepath):
dataframe = read_csv(filepath, header=None, delim_whitespace=True)
return dataframe.values
# load a list of files and return as a 3d numpy array
def load_group(filenames, prefix=''):
loaded = list()
for name in filenames:
data = load_file(prefix + name)
loaded.append(data)
# stack group so that features are the 3rd dimension
loaded = dstack(loaded)
return loaded
# load a dataset group, such as train or test
def load_dataset_group(group, prefix=''):
filepath = prefix + group + '/Inertial Signals/'
# load all 9 files as a single array
filenames = list()
# total acceleration
filenames += ['total_acc_x_'+group+'.txt', 'total_acc_y_'+group+'.txt', 'total_acc_z_'+group+'.txt']
# body acceleration
filenames += ['body_acc_x_'+group+'.txt', 'body_acc_y_'+group+'.txt', 'body_acc_z_'+group+'.txt']
# body gyroscope
filenames += ['body_gyro_x_'+group+'.txt', 'body_gyro_y_'+group+'.txt', 'body_gyro_z_'+group+'.txt']
# load input data
X = load_group(filenames, filepath)
# load class output
y = load_file(prefix + group + '/y_'+group+'.txt')
return X, y
# load the dataset, returns train and test X and y elements
def load_dataset(prefix=''):
# load all train
trainX, trainy = load_dataset_group('train', prefix + 'HARDataset/')
print(trainX.shape, trainy.shape)
# load all test
testX, testy = load_dataset_group('test', prefix + 'HARDataset/')
print(testX.shape, testy.shape)
# zero-offset class values
trainy = trainy - 1
testy = testy - 1
# one hot encode y
trainy = to_categorical(trainy)
testy = to_categorical(testy)
print(trainX.shape, trainy.shape, testX.shape, testy.shape)
return trainX, trainy, testX, testy
# fit and evaluate a model
def evaluate_model(trainX, trainy, testX, testy):
# define model
verbose, epochs, batch_size = 0, 25, 64
n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
# reshape data into time steps of sub-sequences
n_steps, n_length = 4, 32
trainX = trainX.reshape((trainX.shape[0], n_steps, n_length, n_features))
testX = testX.reshape((testX.shape[0], n_steps, n_length, n_features))
# define model
model = Sequential()
model.add(TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu'), input_shape=(None,n_length,n_features)))
model.add(TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu')))
model.add(TimeDistributed(Dropout(0.5)))
model.add(TimeDistributed(MaxPooling1D(pool_size=2)))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(100))
model.add(Dropout(0.5))
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit network
model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
# evaluate model
_, accuracy = model.evaluate(testX, testy, batch_size=batch_size, verbose=0)
return accuracy
# summarize scores
def summarize_results(scores):
print(scores)
m, s = mean(scores), std(scores)
print('Accuracy: %.3f%% (+/-%.3f)' % (m, s))
# run an experiment
def run_experiment(repeats=10):
# load data
trainX, trainy, testX, testy = load_dataset()
# repeat experiment
scores = list()
for r in range(repeats):
score = evaluate_model(trainX, trainy, testX, testy)
score = score * 100.0
print('>#%d: %.3f' % (r+1, score))
scores.append(score)
# summarize results
summarize_results(scores)
# run the experiment
run_experiment()
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# univariate lstm example
from numpy import array
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
# split a univariate sequence into samples
def split_sequence(sequence, n_steps):
X, y = list(), list()
for i in range(len(sequence)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the sequence
if end_ix > len(sequence)-1:
break
# gather input and output parts of the pattern
seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
# define input sequence
raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]
# choose a number of time steps
n_steps = 3
# split into samples
X, y = split_sequence(raw_seq, n_steps)
# reshape from [samples, timesteps] into [samples, timesteps, features]
n_features = 1
X = X.reshape((X.shape[0], X.shape[1], n_features))
# define model
model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=200, verbose=0)
# demonstrate prediction
x_input = array([70, 80, 90])
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)
# univariate stacked lstm example
from numpy import array
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
# split a univariate sequence
def split_sequence(sequence, n_steps):
X, y = list(), list()
for i in range(len(sequence)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the sequence
if end_ix > len(sequence)-1:
break
# gather input and output parts of the pattern
seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
# define input sequence
raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]
# choose a number of time steps
n_steps = 3
# split into samples
X, y = split_sequence(raw_seq, n_steps)
# reshape from [samples, timesteps] into [samples, timesteps, features]
n_features = 1
X = X.reshape((X.shape[0], X.shape[1], n_features))
# define model
model = Sequential()
model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(n_steps, n_features)))
model.add(LSTM(50, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=200, verbose=0)
# demonstrate prediction
x_input = array([70, 80, 90])
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)
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# multivariate cnn example
from numpy import array
from numpy import hstack
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
# split a multivariate sequence into samples
def split_sequences(sequences, n_steps):
X, y = list(), list()
for i in range(len(sequences)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the dataset
if end_ix > len(sequences):
break
# gather input and output parts of the pattern
seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
# define input sequence
in_seq1 = array([10, 20, 30, 40, 50, 60, 70, 80, 90])
in_seq2 = array([15, 25, 35, 45, 55, 65, 75, 85, 95])
out_seq = array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])
# convert to [rows, columns] structure
in_seq1 = in_seq1.reshape((len(in_seq1), 1))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))
# horizontally stack columns
dataset = hstack((in_seq1, in_seq2, out_seq))
# choose a number of time steps
n_steps = 3
# convert into input/output
X, y = split_sequences(dataset, n_steps)
# the dataset knows the number of features, e.g. 2
n_features = X.shape[2]
# define model
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(n_steps, n_features)))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(50, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=1000, verbose=0)
# demonstrate prediction
x_input = array([[80, 85], [90, 95], [100, 105]])
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)
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# univariate stacked lstm example
from numpy import array
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
# split a univariate sequence
def split_sequence(sequence, n_steps):
X, y = list(), list()
for i in range(len(sequence)):
# find the end of this pattern
end_ix = i + n_steps
# check if we are beyond the sequence
if end_ix > len(sequence)-1:
break
# gather input and output parts of the pattern
seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]
X.append(seq_x)
y.append(seq_y)
return array(X), array(y)
# define input sequence
raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90]
# choose a number of time steps
n_steps = 3
# split into samples
X, y = split_sequence(raw_seq, n_steps)
# reshape from [samples, timesteps] into [samples, timesteps, features]
n_features = 1
X = X.reshape((X.shape[0], X.shape[1], n_features))
# define model
model = Sequential()
model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(n_steps, n_features)))
model.add(LSTM(50, activation='relu'))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
# fit model
model.fit(X, y, epochs=200, verbose=0)
# demonstrate prediction
x_input = array([70, 80, 90])
x_input = x_input.reshape((1, n_steps, n_features))
yhat = model.predict(x_input, verbose=0)
print(yhat)
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# multi-headed cnn model
from numpy import mean
from numpy import std
from numpy import dstack
from pandas import read_csv
from matplotlib import pyplot
from keras.utils import to_categorical
from keras.utils.vis_utils import plot_model
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers.merge import concatenate
# load a single file as a numpy array
def load_file(filepath):
dataframe = read_csv(filepath, header=None, delim_whitespace=True)
return dataframe.values
# load a list of files and return as a 3d numpy array
def load_group(filenames, prefix=''):
loaded = list()
for name in filenames:
data = load_file(prefix + name)
loaded.append(data)
# stack group so that features are the 3rd dimension
loaded = dstack(loaded)
return loaded
# load a dataset group, such as train or test
def load_dataset_group(group, prefix=''):
filepath = prefix + group + '/Inertial Signals/'
# load all 9 files as a single array
filenames = list()
# total acceleration
filenames += ['total_acc_x_'+group+'.txt', 'total_acc_y_'+group+'.txt', 'total_acc_z_'+group+'.txt']
# body acceleration
filenames += ['body_acc_x_'+group+'.txt', 'body_acc_y_'+group+'.txt', 'body_acc_z_'+group+'.txt']
# body gyroscope
filenames += ['body_gyro_x_'+group+'.txt', 'body_gyro_y_'+group+'.txt', 'body_gyro_z_'+group+'.txt']
# load input data
X = load_group(filenames, filepath)
# load class output
y = load_file(prefix + group + '/y_'+group+'.txt')
return X, y
# load the dataset, returns train and test X and y elements
def load_dataset(prefix=''):
# load all train
trainX, trainy = load_dataset_group('train', prefix + 'HARDataset/')
print(trainX.shape, trainy.shape)
# load all test
testX, testy = load_dataset_group('test', prefix + 'HARDataset/')
print(testX.shape, testy.shape)
# zero-offset class values
trainy = trainy - 1
testy = testy - 1
# one hot encode y
trainy = to_categorical(trainy)
testy = to_categorical(testy)
print(trainX.shape, trainy.shape, testX.shape, testy.shape)
return trainX, trainy, testX, testy
# fit and evaluate a model
def evaluate_model(trainX, trainy, testX, testy):
verbose, epochs, batch_size = 0, 10, 32
n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]
# head 1
inputs1 = Input(shape=(n_timesteps,n_features))
conv1 = Conv1D(filters=64, kernel_size=3, activation='relu')(inputs1)
drop1 = Dropout(0.5)(conv1)
pool1 = MaxPooling1D(pool_size=2)(drop1)
flat1 = Flatten()(pool1)
# head 2
inputs2 = Input(shape=(n_timesteps,n_features))
conv2 = Conv1D(filters=64, kernel_size=5, activation='relu')(inputs2)
drop2 = Dropout(0.5)(conv2)
pool2 = MaxPooling1D(pool_size=2)(drop2)
flat2 = Flatten()(pool2)
# head 3
inputs3 = Input(shape=(n_timesteps,n_features))
conv3 = Conv1D(filters=64, kernel_size=11, activation='relu')(inputs3)
drop3 = Dropout(0.5)(conv3)
pool3 = MaxPooling1D(pool_size=2)(drop3)
flat3 = Flatten()(pool3)
# merge
merged = concatenate([flat1, flat2, flat3])
# interpretation
dense1 = Dense(100, activation='relu')(merged)
outputs = Dense(n_outputs, activation='softmax')(dense1)
model = Model(inputs=[inputs1, inputs2, inputs3], outputs=outputs)
# save a plot of the model
plot_model(model, show_shapes=True, to_file='multichannel.png')
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# fit network
model.fit([trainX,trainX,trainX], trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
# evaluate model
_, accuracy = model.evaluate([testX,testX,testX], testy, batch_size=batch_size, verbose=0)
return accuracy
# summarize scores
def summarize_results(scores):
print(scores)
m, s = mean(scores), std(scores)
print('Accuracy: %.3f%% (+/-%.3f)' % (m, s))
# run an experiment
def run_experiment(repeats=10):
# load data
trainX, trainy, testX, testy = load_dataset()
# repeat experiment
scores = list()
for r in range(repeats):
score = evaluate_model(trainX, trainy, testX, testy)
score = score * 100.0
print('>#%d: %.3f' % (r+1, score))
scores.append(score)
# summarize results
summarize_results(scores)
# run the experiment
run_experiment()
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import numpy as np
def r_plot(data,delay=1):
transformed=np.zeros([2,len(data)-delay])
transformed[0,:]=data[0:len(data)-delay]
transformed[1,:]=data[delay:len(data)]
rp=np.zeros([len(data)-delay,len(data)-delay])
for i in range(len(rp)):
for j in range(len(rp)):
rp[i,j]=np.linalg.norm(transformed[:,i]-transformed[:,j])
return rp
def cr_plot(data1,data2,delay=1):
transformed=np.zeros([2,len(data1)-delay])
transformed[0,:]=data1[0:len(data1)-delay]
transformed[1,:]=data2[delay:len(data2)]
crp=np.zeros([len(data1)-delay,len(data2)-delay])
for i in range(len(crp)):
for j in range(len(crp)):
crp[i,j]=np.linalg.norm(transformed[:,i]-transformed[:,j])
return crp
def threshold(mat,thresh=0.2):
mini=mat.min()
maxi=mat.max()
trans=(mat-mini)/(maxi-mini)
for i in range(len(trans)):
for j in range(len(trans)):
if trans[i,j]> thresh:
trans[i,j]=1
else:
trans[i,j]=0
return trans
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