def standardize_all_data():
# Standardize all data
# Let data's mean = 0
# standardard deviation = 1
# If data are not loaded, this function will load data first
# Once all data are standardized,
# function get_trajectory will return standardized data
if config.STANDARDIZED is True:
return get_trajectory()
if config.ALL_TRAJECTORY is None:
# Load data set
get_trajectory()
data = config.ALL_TRAJECTORY
mean = data_mean()
std = data_std()
for i in range(len(data)):
data[i] = list(data[i])
# First two elements are matadata, 3rd is data
data[i][3] = (data[i][3] - mean) / std
data[i] = tuple(data[i])
config.ALL_TRAJECTORY = data
config.STANDARDIZED = True
return get_trajectory()
def fft_all_data():
if config.ALL_COMPLEX_DATA is not None:
return config.ALL_COMPLEX_DATA
if config.TRIM_LENGTH is None:
raise Exception("Trim length has not been setted up!")
trimed = trim_data(get_trajectory())
trimedf = fft_data(trimed)
config.ALL_COMPLEX_DATA = trimedf
return trimedf
def data_std():
if config.DATA_STD is not None:
return config.DATA_STD
data = get_trajectory()
data = np.concatenate(data, axis=0)
std = data.std(axis=0)
config.DATA_STD = std
return std
def data_mean():
if config.DATA_MEAN is not None:
return config.DATA_MEAN
data = get_trajectory()
data = np.concatenate(data, axis=0)
mean = data.mean(axis=0)
config.DATA_MEAN = mean
return mean
def get_random_example(n, trim_length, trimed=True, standardize=True):
if standardize:
data = standardize_all_data()
else:
data = get_trajectory()
if trimed:
data = trim_data(data, length=trim_length)
examples = random.choices(data, k=n)
return examples
def data_pca(n_cp=None):
if config.PCA is not None:
return config.PCA
if config.STANDARDIZED is False:
raise Exception("Standardize data before PCA!")
data = get_trajectory()
X = np.concatenate(data, axis=0)
pca = PCA(n_components=n_cp)
pca.fit(X)
config.PCA = pca
return pca
# %%
def autocorr(x):
result = np.correlate(x, x, mode='full')
return result[int(result.size / 2) - 10:]
def drawfft(y):
yf = fft(y)
x = fftfreq(len(y), 1 / 100)
return x, yf
# %%
adam_e = get_trajectory(name='Adam', emotion='e')
adam_i = get_trajectory(name='Adam', emotion='i')
Bonn_e = get_trajectory(name='Bonn', emotion='e')
Bonn_i = get_trajectory(name='Bonn', emotion='i')
n = get_trajectory(emotion='n')
I = get_trajectory(emotion='i')
e = get_trajectory(emotion='e')
alll = get_trajectory()
# %%
plt.figure(figsize=(100, 10))
plt.plot(np.array(adam_e[0]['RVx']))
# %%