diff --git a/skais/ais/ais_points.py b/skais/ais/ais_points.py
index e0981d215f8989f53a1fe5f088b5b9b542631f7c..27b18f2cb6fb2eba754d2ee4766f6ecbe68c6261 100644
--- a/skais/ais/ais_points.py
+++ b/skais/ais/ais_points.py
@@ -8,7 +8,7 @@ from scipy.stats import stats
from skais.ais.ais_trajectory import AISTrajectory
-
+# TODO: remove
def compute_trajectories(df, time_gap, min_size=50, size_limit=500, interpolation_time=None):
n_sample = len(df.index)
result = []
@@ -26,6 +26,7 @@ def compute_trajectories(df, time_gap, min_size=50, size_limit=500, interpolatio
return result
+# TODO: remove
@jit(nopython=True)
def compute_trajectory(times, time_gap, size_limit):
n_samples = len(times)
@@ -92,32 +93,7 @@ class AISPoints:
f"standardization]")
return normalization_type, normalization_dict
- def histogram(self, features, bins=10, ranges=None, label=None, y_field='label'):
- if label is not None:
- tmp = self.df[self.df[y_field] == label]
- else:
- tmp = self.df
- dat = tmp[features]
- h = np.histogramdd(dat.to_numpy(), bins, ranges)[0]
- if h.sum() == 0:
- return np.full(h.shape, 1 / h.size)
- else:
- return h / h.sum()
-
- def disjointed_histogram(self, features, bins, ranges, label=None, y_field='label'):
- if label is not None:
- tmp = self.df[self.df[y_field] == label]
- else:
- tmp = self.df
-
- if type(bins) == int:
- bins = [bins for _ in features]
-
- histograms = []
- for feature, bin, f_range in zip(features, bins, ranges):
- histograms.append(np.histogram(tmp[feature], bin, f_range))
- return histograms
-
+ # TODO: rename
def compute_diff_heading_cog(self):
self.df["diff"] = self.df.apply(lambda x: 180 - abs(abs(x['heading'] - x['cog']) - 180),
axis=1)
@@ -129,48 +105,10 @@ class AISPoints:
self.df = self.df[self.df["heading"] <= 360]
self.df = self.df[self.df["heading"] >= 0]
- def histogram_joint_x_y(self, x_fields=["sog", "diff"], x_nb_bins=10, x_range=[[0, 30], [0, 180]]
- , y_nb_bins=2, y_fields='label', y_range=[0, 1]):
- return self.histogram(x_fields + [y_fields],
- bins=[x_nb_bins for i in x_fields] + [y_nb_bins],
- ranges=x_range + [y_range])
-
- def histogram_x_knowing_y(self, x_fields=["sog", "diff"], x_nb_bins=10, x_range=[[0, 30], [0, 180]]
- , y_nb_bins=2, y_field='label'):
- result = []
- for i in range(y_nb_bins):
- layer = self.histogram(x_fields, bins=x_nb_bins, ranges=x_range, label=i, y_field=y_field)
- result.append(layer)
- return np.stack(result, axis=len(x_fields))
-
- def disjointed_histogram_x_knowing_y(self, features, x_nb_bins=10, x_range=[[0, 1]]
- , y_nb_bins=4, y_field='label'):
- out = []
- for feature, f_range in zip(features, x_range):
- result = []
- for i in range(y_nb_bins):
- layer, _ = np.histogram(self.df[self.df[y_field] == i][feature].to_numpy(), bins=x_nb_bins,
- range=f_range)
- if layer.sum() == 0:
- layer = np.full(layer.shape, 1)
- result.append(layer)
- out.append(np.stack(result))
- return out
-
- def histogram_y_knowing_x(self, x_fields=["sog", "diff"], x_nb_bins=10, x_range=[[0, 30], [0, 180]]
- , y_nb_bins=2, y_field='label', y_range=[0, 1]):
- h_joint = self.histogram_joint_x_y(x_fields, x_nb_bins, x_range, y_nb_bins, y_field, y_range)
- y_hist = self.histogram(features=y_field, bins=y_nb_bins, ranges=[y_range])
-
- result = np.zeros(h_joint.shape)
-
- for idx, x in np.ndenumerate(h_joint):
- if h_joint[idx[:-1]].sum() == 0:
- result[idx] = y_hist[idx[-1]]
- else:
- result[idx] = x / h_joint[idx[:-1]].sum()
- return result
+
+
+ # TODO: redo
def get_trajectories(self, time_gap=30, min_size=50, interpolation_time=None):
if 'ts' in self.df:
diff --git a/skais/learn/point_representation.py b/skais/learn/point_representation.py
new file mode 100644
index 0000000000000000000000000000000000000000..beaad6b5dc54a6fa107b4632ff98374c275a15f2
--- /dev/null
+++ b/skais/learn/point_representation.py
@@ -0,0 +1,71 @@
+import numpy as np
+
+
+def histogram(ais_points, features, bins, ranges=None, label=None, y_field='label'):
+ if label is not None:
+ tmp = ais_points.df[ais_points.df[y_field] == label]
+ else:
+ tmp = ais_points.df
+ dat = tmp[features]
+ h = np.histogramdd(dat.to_numpy(), bins, ranges)[0]
+ if h.sum() == 0:
+ return np.full(h.shape, 1 / h.size)
+ else:
+ return h / h.sum()
+
+
+def disjointed_histogram(ais_points, features, bins, ranges, label=None, y_field='label'):
+ if label is not None:
+ tmp = ais_points.df[ais_points.df[y_field] == label]
+ else:
+ tmp = ais_points.df
+
+ if type(bins) == int:
+ bins = [bins for _ in features]
+
+ histograms = []
+ for feature, h_bin, f_range in zip(features, bins, ranges):
+ histograms.append(np.histogram(tmp[feature], h_bin, f_range))
+ return histograms
+
+
+def histogram_joint_x_y(ais_points, x_fields, x_nb_bins, x_range, y_fields, y_nb_bins, y_range):
+ return histogram(ais_points, x_fields + [y_fields],
+ bins=[x_nb_bins for _ in x_fields] + [y_nb_bins],
+ ranges=x_range + [y_range])
+
+
+def histogram_x_knowing_y(ais_points, x_fields, x_nb_bins, x_range, y_nb_bins, y_field):
+ result = []
+ for i in range(y_nb_bins):
+ layer = histogram(ais_points, x_fields, bins=x_nb_bins, ranges=x_range, label=i, y_field=y_field)
+ result.append(layer)
+ return np.stack(result, axis=len(x_fields))
+
+
+def disjointed_histogram_x_knowing_y(ais_points, features, x_nb_bins, x_range, y_nb_bins, y_field):
+ out = []
+ for feature, f_range in zip(features, x_range):
+ result = []
+ for i in range(y_nb_bins):
+ layer, _ = np.histogram(ais_points.df[ais_points.df[y_field] == i][feature].to_numpy(), bins=x_nb_bins,
+ range=f_range)
+ if layer.sum() == 0:
+ layer = np.full(layer.shape, 1)
+ result.append(layer)
+ out.append(np.stack(result))
+ return out
+
+
+def histogram_y_knowing_x(ais_points, x_fields, x_nb_bins, x_range, y_nb_bins, y_field, y_range):
+ h_joint = histogram_joint_x_y(ais_points, x_fields, x_nb_bins, x_range, y_nb_bins, y_field, y_range)
+ y_hist = histogram(ais_points, features=y_field, bins=y_nb_bins, ranges=[y_range])
+
+ result = np.zeros(h_joint.shape)
+
+ for idx, x in np.ndenumerate(h_joint):
+ if h_joint[idx[:-1]].sum() == 0:
+ result[idx] = y_hist[idx[-1]]
+ else:
+ result[idx] = x / h_joint[idx[:-1]].sum()
+ return result