def predict(self, data_path, infant_id):
if self.verbose:
print(
f"Predicting infant {infant_id} - {strftime('%H:%M:%S', gmtime())}"
)
window_sizes = [128, 256, 512, 1024]
etl = ETL(data_path,
window_sizes,
pooling="mean",
sma_window=3,
bandwidth=0,
minimal_movement=0.75)
etl.load_infant(infant_id)
if self.verbose:
print(f"Preprocessing the data - {strftime('%H:%M:%S', gmtime())}")
etl.preprocess_pooled()
angles = [
"right_shoulder", "left_shoulder", "right_elbow", "left_elbow",
"right_hip", "left_hip", "right_knee", "left_knee"
]
predictions = {}
video_length = len(etl.cima[infant_id]["data"])
prediction = Prediction(video_length)
for angle in angles:
predictions[angle] = pd.Series(
[[] for i in range(len(etl.cima[infant_id]["data"]))])
if self.verbose:
print(
f"Generating fourier data - {strftime('%H:%M:%S', gmtime())}")
for window_size in window_sizes:
for angle in angles:
dataframe = etl.generate_fourier_data(angle, window_size,
window_size // 4)
data_features = pd.DataFrame(dataframe.data.tolist())
if not data_features.empty:
data_transformed = self.model[window_size][
"pls"].transform(data_features)
dataframe["label"] = self.model[window_size][
"model"].predict_proba(data_transformed)
else:
dataframe["label"] = pd.Series([])
prediction.set_window_data(window_size, angle, dataframe)
infant = etl.cima[infant_id]
infant["predictions"] = prediction
return infant, prediction
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import colors
from matplotlib.ticker import PercentFormatter
window_sizes = [128, 256, 512, 1024]
etl = ETL("/home/erlend/datasets", window_sizes)
etl.load("CIMA")
etl.preprocess_pooled()
angles = etl.angles.keys()
differences = {}
for window_size in tqdm(window_sizes):
etl.differences = []
for angle in angles:
etl.generate_fourier_data(angle, window_size, window_size)
differences[window_size] = etl.differences
bins = [0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.25, 2.5]
for window_size in window_sizes:
fig = plt.figure()
plt.hist(differences[window_size],
color="mediumslateblue",
bins=bins,
weights=np.ones(len(differences[window_size])) /
len(differences[window_size]),
edgecolor="black",
linewidth=0.5)
plt.xticks(bins)
plt.xlim(0, 2.5)