In [1]:
import pandas as pd
import numpy as np
import pickle
import os
from scipy.stats import kurtosis, skew
from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout, Activation, Flatten
from keras.layers import Conv1D, MaxPooling1D, GlobalAveragePooling1D
from keras.layers.normalization import BatchNormalization
from keras.utils import to_categorical
from keras.optimizers import Adam
from keras.models import load_model
from sklearn import metrics
import matplotlib.pyplot as plt
from scipy.signal import butter, lfilter, freqz
In [2]:
'''LABELS = [
"HAZMEI'S SEXY POSE (NEUTRAL)",
"HAZMEI'S FLYING KISS (WAVE HANDS)",
"HYUN'S MAD DRIVING SKILLS (BUS DRIVING)",
"ENCIK'S IPPT 2.4KM (FRONT BACK)",
"HYUN'S BALLET DANCE (SIDE STEP)",
"HAZMEI'S BELLY BOUNCE (JUMPING)",
"JUMPING JACK DR. WANG YE VERSION",
"TURN CLAP",
"SQUAT TURN CLAP",
"WINDOW",
"WINDOW 360",
"MONEY (FINAL MOVE)"
] '''
LABELS = [
"HAZMEI'S SEXY POSE (NEUTRAL)",
"HAZMEI'S FLYING KISS (WAVE HANDS)",
"HYUN'S MAD DRIVING SKILLS (BUS DRIVING)",
"ENCIK'S IPPT 2.4KM (FRONT BACK)",
"HYUN'S BALLET DANCE (SIDE STEP)",
"HAZMEI'S BELLY BOUNCE (JUMPING)",
"JUMPING JACK DR. WANG YE VERSION",
"TURN CLAP",
"SQUAT TURN CLAP",
"WINDOW",
"WINDOW 360",
"MONEY (FINAL MOVE)"
]
SAMPLING_RATE = 50
WINDOW_SIZE = 2.5
WINDOW_READINGS = int(WINDOW_SIZE * SAMPLING_RATE)
DATAPATH = 'data_all/'
ORDER = 3 # Order 3
CUTOFF = 7 # desired cutoff frequency of the filter in Hz (take max/60)
FILTER_SAMPLING_RATE = 20
In [3]:
def normalize_data(data):
data_norm = (data - data.mean()) / (data.max() - data.min())
return np.array(data_norm)
def frequency(data):
fourier = np.fft.fft(data)
freqs = np.fft.fftfreq(len(data), d=1/SAMPLING_RATE)
def magnitude(x, y, z):
x_sq = np.power(x, 2)
y_sq = np.power(y, 2)
z_sq = np.power(z, 2)
xyz_sq = x_sq + y_sq + z_sq
xyz_mag = np.sqrt(xyz_sq)
return xyz_mag
def rms(x, axis=None):
return np.sqrt(np.mean(np.power(x, 2), axis=axis))
def feature_extraction(x, y, z):
#'''
#mean, std
features = [np.mean(x), np.mean(y), np.mean(z), np.std(x), np.std(y), np.std(z)]
#Median Absolute Deviation
features.extend((np.mean(abs(x - features[0])), np.mean(abs(y - features[1])), np.mean(abs(z - features[2]))))
#Jerk Signals mean, std, mad
features.extend((np.mean(np.diff(x)), np.mean(np.diff(y)), np.mean(np.diff(z)), np.std(np.diff(x)), np.std(np.diff(y)), np.std(np.diff(z))))
features.extend((np.mean(abs(np.diff(x) - features[9])), np.mean(abs(np.diff(y) - features[10])), np.mean(abs(np.diff(y) - features[11]))))
#max, min
features.extend((max(x), max(y), max(z), min(x), min(y), min(z)))
#correlation
features.extend((np.correlate(x, y)[0], np.correlate(x, z)[0], np.correlate(y, z)[0]))
#energy
features.extend((np.dot(x,x)/len(x), np.dot(y,y)/len(y), np.dot(z,z)/len(z)))
#iqr
#features.extend((np.subtract(*np.percentile(x, [75, 25])), np.subtract(*np.percentile(y, [75, 25])), np.subtract(*np.percentile(z, [75, 25]))))
#Root Mean Square
features.extend((rms(x), rms(y), rms(z)))
#Skew, Kurtosis
features.extend((skew(x), skew(y), skew(z), kurtosis(x), kurtosis(y), kurtosis(z)))
#'''
'''
#Frequency Domain Features
fourier_x = np.fft.fft(x)
fourier_y = np.fft.fft(x)
fourier_z = np.fft.fft(x)
freqs = np.fft.fftfreq(WINDOW_READINGS)
fourier_x = np.abs(fourier_x)
fourier_y = np.abs(fourier_y)
fourier_z = np.abs(fourier_z)
#Mean Frequency, Skew, Kurtosis
features.extend((np.mean(fourier_x), np.mean(fourier_y), np.mean(fourier_z)))
features.extend((skew(fourier_x), skew(fourier_y), skew(fourier_z), kurtosis(fourier_x), kurtosis(fourier_y), kurtosis(fourier_z)))
'''
'''
#Old Feature Extraction
features = [np.mean(x), np.mean(y), np.mean(z), np.std(x), np.std(y), np.std(z)]
#Median Absolute Deviation
features.extend((np.mean(abs(x - features[0])), np.mean(abs(y - features[1])), np.mean(abs(z - features[2]))))
#Jerk Signals
features.extend((np.mean(np.diff(x)), np.mean(np.diff(y)), np.mean(np.diff(z)), np.std(np.diff(x)), np.std(np.diff(y)), np.std(np.diff(z))))
features.extend((np.mean(abs(np.diff(x) - features[9])), np.mean(abs(np.diff(y) - features[10])), np.mean(abs(np.diff(y) - features[11]))))
features.extend((skew(x), skew(y), skew(z), kurtosis(x), kurtosis(y), kurtosis(z)))
features.extend((max(x), max(y), max(z), min(x), min(y), min(z)))
'''
return features
def add_noise(data):
data_noise = data + np.random.uniform(size=len(data))
data_noise = data_noise + np.random.laplace(loc=0.0, scale=1.0, size=len(data))
return data_noise
def data_augmentation(X):
X_noise = X
for i in range(X.shape[0]):
for j in range(X.shape[2]):
X_noise[i][:][j] = add_noise(X_noise[i][:][j])
return np.concatenate((X, X_noise), axis=0)
def feature_selection(X, augmentData=False):
data = []
for i in range(X.shape[0]):
features = []
for j in range(0, X.shape[2], 3):
x = [X[i][u][j] for u in range(X.shape[1])]
y = [X[i][u][j+1] for u in range(X.shape[1])]
z = [X[i][u][j+2] for u in range(X.shape[1])]
if augmentData:
x_noise = add_noise(x)
y_noise = add_noise(y)
z_noise = add_noise(z)
features.append(feature_extraction(x_noise, y_noise, z_noise))
else:
features.append(feature_extraction(x, y, z))
data.append(features)
return np.array(data)
def feature_engineering(X, augmentData=False):
if augmentData:
return np.concatenate((feature_selection(X, False), feature_selection(X, True)), axis=0)
else:
return feature_selection(X, False)
def shitHotLP(data, cutoff, fs, order):
b, a = butter_lowpass(cutoff, fs, order)
v = butter_lowpass_filter(data, cutoff, fs, order)
return v
def butter_lowpass(cutoff, fs, order):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
b, a = butter(order, normal_cutoff, btype='low', analog=False)
return b, a
def butter_lowpass_filter(data, cutoff, fs, order):
b, a = butter_lowpass(cutoff, fs, order=order)
y = lfilter(b, a, data)
return y
def read_data(filename, label):
raw_data = pd.read_csv(filename)
raw_data = raw_data.iloc[100:-50, 0:9]
raw_data = raw_data[:raw_data.shape[0]-(raw_data.shape[0]%WINDOW_READINGS)]
#print(raw_data.shape)
#print(filename, ': Minimum: ', np.min(raw_data))
#print(filename, ': Maximum: ', np.max(raw_data))
normalized_data = shitHotLP(raw_data, CUTOFF, FILTER_SAMPLING_RATE, ORDER)
sampled_data = normalized_data.reshape(-1, WINDOW_READINGS, 9)
#print(sampled_data.shape)
return sampled_data, [label]*sampled_data.shape[0]
def import_data(root_dirpath, test_data_size):
X = np.zeros([1,WINDOW_READINGS,9])
Xt = np.zeros([1,WINDOW_READINGS,9])
y = []
yt = []
sub_directories = next(os.walk(root_dirpath))[1]
for sub_dir in sub_directories:
files = next(os.walk(root_dirpath + sub_dir))[2]
count = 0
samples = 0
for file in files:
if not file or file == '.DS_Store':
continue
#print(file)
temp_x, temp_y = read_data(root_dirpath + sub_dir + '/' + file, int(sub_dir))
if (count < test_data_size):
print(file, ' size: ', len(temp_y))
Xt = np.concatenate((Xt, temp_x), axis=0)
yt = yt + temp_y
else:
X = np.concatenate((X, temp_x), axis=0)
y = y + temp_y
count = count + 1 if len(temp_y) > 40 else count + 0.5
samples += len(temp_y)
print(LABELS[y[-1]], ': ', samples)
y = np.array(y)
yt = np.array(yt)
return X[1:], y, Xt[1:], yt
In [4]:
#Prediction
def predict(model):
Y_output = model.predict(Xt)
Y_pred = np.argmax(Y_output, axis=1)
print(np.array(Y_pred))
print("")
print("Accuracy Rate:")
print(metrics.accuracy_score(Yt, Y_pred))
print("")
print("Confusion Matrix:")
confusion_matrix = metrics.confusion_matrix(Yt, Y_pred)
print(confusion_matrix)
normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100
'''
print("Precision: {}%".format(100*metrics.precision_score(Yt, Y_pred, average="weighted")))
print("Recall: {}%".format(100*metrics.recall_score(Yt, Y_pred, average="weighted")))
print("f1_score: {}%".format(100*metrics.f1_score(Yt, Y_pred, average="weighted")))
print("")
print("Confusion matrix (normalised to % of total test data):")
print(normalised_confusion_matrix)
'''
# Plot Results:
plt.figure(figsize=(12, 12))
plt.imshow(
normalised_confusion_matrix,
interpolation='nearest',
cmap=plt.cm.rainbow
)
plt.title("Confusion matrix \n(normalised to % of total test data)")
plt.colorbar()
tick_marks = np.arange(len(LABELS))
plt.xticks(tick_marks, LABELS, rotation=90)
plt.yticks(tick_marks, LABELS)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
np.savetxt(DATAPATH+"accuracy.csv", [metrics.accuracy_score(Yt, Y_pred)], delimiter=",")
np.savetxt(DATAPATH+"confusion_matrix.csv", metrics.confusion_matrix(Yt, Y_pred), delimiter=",")
model.save(DATAPATH+'trained_nn_model.h5')
In [5]:
X, Y, Xt, Yt = import_data(DATAPATH, 1)
print(X.shape)
print(Y.shape)
print(Xt.shape)
print(Yt.shape)
#print(X)
#print(Y)
print(Yt)
In [6]:
#Plot Data
Size = Xt.shape[1];
Sample = [230, 180]
legends = ['acc1x', 'acc1y', 'acc1z', 'acc1x', 'acc2y', 'acc3z', 'gyrox', 'gyroy', 'gyroz']
for i in Sample:
print(Yt[i])
plt.figure(figsize=(9,9))
plt.plot(list(range(Size)), Xt[i][:][:], label=legends )
plt.show()
temp = Xt[i][:][:].T
print(temp.shape)
#print(temp[0])
print(np.correlate(temp[0], temp[1])[0])
fourier = np.fft.fft(temp[8])
freqs = np.fft.fftfreq(temp.shape[1])
plt.plot(freqs, fourier.real, freqs, fourier.imag)
plt.show()
plt.plot(freqs, np.abs(fourier)**2)
plt.show()
idx = np.argsort(freqs)
plt.plot(freqs[idx], fourier[idx])
plt.show()
print(max(freqs))
print(np.mean(np.abs(fourier)**2))
#print(fourier)
#print(freqs)
#print(np.abs(fourier))
#print(np.mean(fourier))
#print(freqs)
#print(np.abs(freqs[0:51]))
In [7]:
#Raw Data with Data Augmentation
'''
X = data_augmentation(X)
Xt = data_augmentation(Xt)
Y = np.concatenate((Y, Y), axis=0)
Yt = np.concatenate((Yt, Yt), axis=0)
'''
#Feature Selection & Data Augmentation
#X = feature_engineering(X, False)
#Xt = feature_engineering(Xt, False)
#Y = np.concatenate((Y, Y), axis=0)
#Yt = np.concatenate((Yt, Yt), axis=0)
Yhot = to_categorical(Y)
print(X.shape)
print(Xt.shape)
print(Yhot.shape)
#print([Xt[i] for i in Sample])
np.savetxt("data/test_data_format.csv", Xt[0], delimiter=",")
#X = X.reshape(X.shape[0], X.shape[1]*X.shape[2])
#Xt = Xt.reshape(Xt.shape[0], Xt.shape[1]*Xt.shape[2])
In [8]:
'''#Neural Network
data_dim = X.shape[2]
timesteps = X.shape[1]
num_classes = Yhot.shape[1]
b_size = 32
model = Sequential()
model.add(Dense(70, input_shape=(timesteps, data_dim)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(70))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(70))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, Yhot, epochs=50, batch_size=b_size, validation_split=0.1, shuffle=True)
result = model.evaluate(X, Yhot)
print("\n%s: %.2f%%" % (model.metrics_names[0], result[0]))
print("\n%s: %.2f%%" % (model.metrics_names[1], result[1]*100))
predict(model)'''
Out[8]:
In [9]:
#CNN Neural Network
data_dim = X.shape[2]
timesteps = X.shape[1]
num_classes = Yhot.shape[1]
b_size = 32
model = Sequential()
model.add(Conv1D(128, 4, padding='valid', strides=1, input_shape=(timesteps, data_dim)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling1D())
model.add(Dropout(0.2))
model.add(Conv1D(128, 2, padding='valid', strides=1) )
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Conv1D(64, 2, padding='valid', strides=1) )
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling1D())
model.add(Dropout(0.2))
model.add(Conv1D(32, 1, padding='valid', strides=1) )
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(GlobalAveragePooling1D())
model.add(Dense(num_classes, activation='softmax'))
'''model = Sequential()
model.add(Conv1D(64, 4, padding='valid', activation='relu', strides=1, input_shape=(timesteps, data_dim)))
model.add(MaxPooling1D())
model.add(Dropout(0.2))
model.add(Conv1D(64, 2, padding='valid', activation='relu', strides=1) )
model.add(Conv1D(32, 2, padding='valid', activation='relu', strides=1) )
model.add(MaxPooling1D())
model.add(Dropout(0.2))
model.add(Conv1D(32, 1, padding='valid', activation='relu', strides=1) )
model.add(GlobalAveragePooling1D())
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))'''
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, Yhot, epochs=50, batch_size=b_size, validation_split=0.1, shuffle=True)
result = model.evaluate(X, Yhot)
print("\n%s: %.2f%%" % (model.metrics_names[0], result[0]))
print("\n%s: %.2f%%" % (model.metrics_names[1], result[1]*100))
predict(model)
In [10]:
#LSTM Neural Network
'''data_dim = X.shape[2]
timesteps = X.shape[1]
num_classes = Yhot.shape[1]
b_size = 32
model = Sequential()
model.add(LSTM(64, return_sequences=True, input_shape=(timesteps, data_dim)))
model.add(Dropout(0.7))
model.add(LSTM(64, return_sequences=True))
model.add(Dropout(0.7))
model.add(LSTM(64, return_sequences=True))
model.add(Flatten())
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, Yhot, epochs=5, batch_size=b_size, validation_split=0.1, shuffle=True)
result = model.evaluate(X, Yhot)
print("\n%s: %.2f%%" % (model.metrics_names[0], result[0]))
print("\n%s: %.2f%%" % (model.metrics_names[1], result[1]*100))
predict(model)'''
Out[10]:
In [11]:
'''
raw_data = pd.read_csv('data/sample_data_format.csv', skiprows=range(0, 7))
print(raw_data.shape)
cropped_data = raw_data.values.reshape(-1, 40, 9)
print(cropped_data.shape)
print(cropped_data)
pickle.dump(cropped_data, open('data/cropped_data_format.pkl', 'wb'))
np.savetxt("data/cropped_data_format_2.csv", cropped_data[2], delimiter=",")
'''
Out[11]:
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