Rename ml as RofuncML

examples/learning_ml/example_birp.py

@@ -110,7 +110,7 @@ def learn(demos_left_x, demos_right_x, demo_idx=0):
                              'frame_names': ['start', 'via1', 'via2', 'end']}}
 
     # Create representation model
-    representation = rf.ml.TPGMM_RPCtrl(demos_left_x, demos_right_x, nb_states=4, plot=True, save=False,
+    representation = rf.RofuncML.TPGMM_RPCtrl(demos_left_x, demos_right_x, nb_states=4, plot=True, save=False,
                                           save_params=save_params, task_params=task_params)
 
     # Define observe frames for new situation
@@ -129,7 +129,7 @@ def learn(demos_left_x, demos_right_x, demo_idx=0):
                    'right': {'frame_origins': [start_xdx_r, via1_xdx_r, via2_xdx_r, end_xdx_r],
                              'frame_names': ['start', 'via1', 'via2', 'end']}}
     # 'traj': representation.repr_r.demos_x[demo_idx]}}
-    if isinstance(representation, rf.ml.TPGMM_RPCtrl) or isinstance(representation, rf.ml.TPGMM_RPAll):
+    if isinstance(representation, rf.RofuncML.TPGMM_RPCtrl) or isinstance(representation, rf.RofuncML.TPGMM_RPAll):
         model_l, model_r, model_c = representation.fit()
         representation.reproduce(model_l, model_r, model_c, show_demo_idx=demo_idx)
         traj_l, traj_r, _, _ = representation.generate(model_l, model_r, model_c, ref_demo_idx=demo_idx,

examples/learning_ml/example_felt.py

@@ -67,7 +67,7 @@ def get_orientation(df):
 end_xdx = [demos_x[i][-1] for i in range(len(demos_x))]
 task_params = {'frame_origins': [start_xdx, end_xdx], 'frame_names': ['start', 'end']}
 # Fit the model
-Repr = rf.ml.TPGMM(demos_x, task_params, plot=True, nb_states=100)
+Repr = rf.RofuncML.TPGMM(demos_x, task_params, plot=True, nb_states=100)
 model = Repr.fit()
 
 # Reproductions for the same situations

examples/learning_ml/example_gmr.py

@@ -14,5 +14,5 @@
 demos_dx = data['dx']  # Velocity data
 demos_xdx = [np.hstack([_x, _dx]) for _x, _dx in zip(demos_x, demos_dx)]  # Position-velocity
 
-representation = rf.ml.GMR(demos_x, demos_dx, demos_xdx, plot=True)
+representation = rf.RofuncML.GMR(demos_x, demos_dx, demos_xdx, plot=True)
 representation.fit()

examples/learning_ml/example_taichi.py

@@ -27,7 +27,7 @@ def uni():
     demos_x = np.vstack((raw_demo[:, 430:525, :], raw_demo[:, 240:335, :], raw_demo[:, 335:430, :]))
 
     # Reproductions for the same situations
-    representation = rf.ml.tpgmm.TPGMM(demos_x)
+    representation = rf.RofuncML.tpgmm.TPGMM(demos_x)
     model = representation.fit(plot=True)
 
     # Reproductions for new situations
@@ -66,7 +66,7 @@ def bi():
     task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
                    'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
 
-    representation = rf.ml.tpgmm.TPGMMBi(demos_left_x, demos_right_x, task_params, plot=True)
+    representation = rf.RofuncML.tpgmm.TPGMMBi(demos_left_x, demos_right_x, task_params, plot=True)
     model_l, model_r = representation.fit()
 
     # # Reproductions for the same situations

examples/learning_ml/example_tpgmm.py

@@ -17,7 +17,7 @@
 end_xdx = [demos_x[i][-1] for i in range(len(demos_x))]
 task_params = {'frame_origins': [start_xdx, end_xdx], 'frame_names': ['start', 'end']}
 # Fit the model
-Repr = rf.ml.TPGMM(demos_x, task_params, plot=True)
+Repr = rf.RofuncML.TPGMM(demos_x, task_params, plot=True)
 model = Repr.fit()
 
 # Reproductions for the same situations

examples/learning_ml/example_tpgmmbi.py

@@ -21,7 +21,7 @@
 task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
                'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
 # Fit the model
-Repr = rf.ml.TPGMMBi(demos_left_x, demos_right_x, task_params, plot=True)
+Repr = rf.RofuncML.TPGMMBi(demos_left_x, demos_right_x, task_params, plot=True)
 model_l, model_r = Repr.fit()
 
 # Reproductions for the same situations

examples/learning_ml/example_tpgmmbi_RPCtrl.py

@@ -27,7 +27,7 @@
 task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
                'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
 # Fit the model
-Repr = rf.ml.TPGMM_RPCtrl(demos_left_x, demos_right_x, task_params, plot=True)
+Repr = rf.RofuncML.TPGMM_RPCtrl(demos_left_x, demos_right_x, task_params, plot=True)
 model_l, model_r, model_c = Repr.fit()
 
 # Reproductions for the same situations

examples/learning_ml/example_tpgmmbi_RPRepr.py

@@ -26,7 +26,7 @@
 task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
                'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
 # Fit the model
-Repr = rf.ml.TPGMM_RPRepr(demos_left_x, demos_right_x, task_params, plot=True)
+Repr = rf.RofuncML.TPGMM_RPRepr(demos_left_x, demos_right_x, task_params, plot=True)
 model_l, model_r = Repr.fit()
 
 # Reproductions for the same situations

examples/learning_ml/example_tpgmr.py

@@ -17,7 +17,7 @@
 end_xdx = [demos_x[i][-1] for i in range(len(demos_x))]
 task_params = {'frame_origins': [start_xdx, end_xdx], 'frame_names': ['start', 'end']}
 # Fit the model
-Repr = rf.ml.TPGMR(demos_x, task_params, plot=True)
+Repr = rf.RofuncML.TPGMR(demos_x, task_params, plot=True)
 model = Repr.fit()
 
 # Reproductions for the same situations

rofunc/__init__.py

@@ -18,8 +18,7 @@
 
 from .devices import zed, xsens, optitrack, mmodal, emg
 from . import simulator as sim
-from .learning import RofuncML
-from .learning import RofuncIL, RofuncRL
+from .learning import RofuncML, RofuncIL, RofuncRL
 from .planning_control import lqt, lqr
 from .utils import visualab, robolab, logger, oslab, maniplab
 from .utils.robolab import ergonomics

rofunc/learning/RofuncML/tpgmm.py

@@ -143,7 +143,7 @@ def hmm_learning(self) -> HMM:
         model.init_hmm_kbins(self.demos_xdx_augm)  # initializing model
         model.em(self.demos_xdx_augm, reg=self.reg)
 
-        fig = rf.ml.hmm_plot(self.nb_dim, self.demos_xdx_f, model, self.task_params)
+        fig = rf.RofuncML.hmm_plot(self.nb_dim, self.demos_xdx_f, model, self.task_params)
         if self.save:
             rf.visualab.save_img(fig, self.save_params['save_dir'], format=self.save_params['format'])
         if self.plot:
@@ -176,7 +176,7 @@ def poe(self, model: HMM, show_demo_idx: int) -> GMM:
         for p in range(1, self.nb_frames):
             prod *= mod_list[p]
 
-        fig = rf.ml.poe_plot(self.nb_dim, mod_list, prod, self.demos_x, show_demo_idx, self.task_params)
+        fig = rf.RofuncML.poe_plot(self.nb_dim, mod_list, prod, self.demos_x, show_demo_idx, self.task_params)
         if self.save:
             rf.visualab.save_img(fig, self.save_params['save_dir'], format=self.save_params['format'])
         if self.plot:
@@ -207,7 +207,7 @@ def _reproduce(self, model: HMM, prod: GMM, show_demo_idx: int, start_xdx: np.nd
 
         xi = lqr.seq_xi
 
-        fig = rf.ml.gen_plot(self.nb_dim, xi, prod, self.demos_x, show_demo_idx, title=title, label=label)
+        fig = rf.RofuncML.gen_plot(self.nb_dim, xi, prod, self.demos_x, show_demo_idx, title=title, label=label)
         if self.save:
             rf.visualab.save_img(fig, self.save_params['save_dir'], format=self.save_params['format'])
         if self.plot:
@@ -573,7 +573,7 @@ def _uni_poe(self, model, repr, show_demo_idx, task_params=None) -> GMM:
         prod = mod_list[0] * mod_list[1] * mod_list[2] * mod_list[2] * mod_list[2] * mod_list[2] * mod_list[2] * \
                mod_list[2] * mod_list[2]
 
-        fig = rf.ml.poe_plot(self.nb_dim, mod_list, prod, repr.demos_x, show_demo_idx, repr.task_params)
+        fig = rf.RofuncML.poe_plot(self.nb_dim, mod_list, prod, repr.demos_x, show_demo_idx, repr.task_params)
         if self.save:
             rf.visualab.save_img(fig, self.save_params['save_dir'], format=self.save_params['format'])
         if self.plot:

rofunc/learning/RofuncML/tpgmr.py

@@ -71,9 +71,9 @@ def _reproduce(self, model: pbd.HMM, prod: pbd.GMM, show_demo_idx: int, start_xd
         xi = lqr.seq_xi
         if self.plot:
             if len(self.demos_x[0][0]) == 2:
-                rf.ml.generate_plot(xi, prod, self.demos_x, show_demo_idx)
+                rf.RofuncML.generate_plot(xi, prod, self.demos_x, show_demo_idx)
             elif len(self.demos_x[0][0]) > 2:
-                rf.ml.generate_plot_3d(xi, prod, self.demos_x, show_demo_idx, scale=0.1)
+                rf.RofuncML.generate_plot_3d(xi, prod, self.demos_x, show_demo_idx, scale=0.1)
             else:
                 raise Exception('Dimension is less than 2, cannot plot')
         return xi

rofunc/learning/RofuncML/tphsmm.py

@@ -76,7 +76,7 @@ def hsmm_learning(self):
         self.hsmm.em(self.demos_tp, reg=self.reg)
 
         if self.plot:
-            rf.ml.hmm_plot(self.nb_dim, self.demos_tp_f, self.hsmm)
+            rf.RofuncML.hmm_plot(self.nb_dim, self.demos_tp_f, self.hsmm)
 
     def poe(self, show_demo_idx: int, task_params: tuple = None) -> pbd.GMM:
         """
@@ -108,7 +108,7 @@ def poe(self, show_demo_idx: int, task_params: tuple = None) -> pbd.GMM:
                 prod = prod * mod
 
         if self.plot:
-            rf.ml.poe_plot(self.nb_dim, [marginal_models[0], marginal_models[1]], prod, self.demos_x, show_demo_idx)
+            rf.RofuncML.poe_plot(self.nb_dim, [marginal_models[0], marginal_models[1]], prod, self.demos_x, show_demo_idx)
         return prod
 
     def _reproduce(self, prod: pbd.GMM, show_demo_idx: int, start_xdx: np.ndarray,
@@ -136,7 +136,7 @@ def _reproduce(self, prod: pbd.GMM, show_demo_idx: int, start_xdx: np.ndarray,
 
         xi = lqr.seq_xi
         if self.plot:
-            rf.ml.gen_plot(self.nb_dim, xi, prod, self.demos_x, show_demo_idx)
+            rf.RofuncML.gen_plot(self.nb_dim, xi, prod, self.demos_x, show_demo_idx)
         return xi
 
     def _generate(self, prod: pbd.GMM, ref_demo_idx: int,
@@ -171,7 +171,7 @@ def _generate(self, prod: pbd.GMM, ref_demo_idx: int,
 
         xi = lqr.seq_xi
         if self.plot:
-            rf.ml.gen_plot(self.nb_dim, xi, prod, self.demos_x, ref_demo_idx)
+            rf.RofuncML.gen_plot(self.nb_dim, xi, prod, self.demos_x, ref_demo_idx)
         return xi
 
     def fit(self) -> pbd.HSMM:

tests/test_tpgmm.py

@@ -12,7 +12,7 @@ def test_2d_uni_tpgmm():
     start_xdx = [demos_x[i][0] for i in range(len(demos_x))]  # TODO: change to xdx
     end_xdx = [demos_x[i][-1] for i in range(len(demos_x))]
     task_params = {'frame_origins': [start_xdx, end_xdx], 'frame_names': ['start', 'end']}
-    Repr = rf.ml.TPGMM(demos_x, task_params, plot=False)
+    Repr = rf.RofuncML.TPGMM(demos_x, task_params, plot=False)
     model = Repr.fit()
 
     # Reproductions for the same situations
@@ -36,63 +36,63 @@ def test_2d_bi_tpgmm():
     end_xdx_r = [demos_right_x[i][-1] for i in range(len(demos_right_x))]
     task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
                    'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
-    Repr = rf.ml.TPGMMBi(demos_left_x, demos_right_x, task_params, plot=False)
+    Repr = rf.RofuncML.TPGMMBi(demos_left_x, demos_right_x, task_params, plot=False)
     model_l, model_r = Repr.fit()
 
     traj_l, traj_r, _, _ = Repr.reproduce([model_l, model_r], show_demo_idx=2)
 
 
-def test_7d_uni_tpgmm():
-    raw_demo = np.load('../examples/data/LFD_ML/LeftHand.npy')
-    demos_x = [raw_demo[500:635, :], raw_demo[635:770, :], raw_demo[770:905, :]]
-
-    start_xdx = [demos_x[i][0] for i in range(len(demos_x))]  # TODO: change to xdx
-    end_xdx = [demos_x[i][-1] for i in range(len(demos_x))]
-    task_params = {'frame_origins': [start_xdx, end_xdx], 'frame_names': ['start', 'end']}
-    Repr = rf.ml.TPGMM(demos_x, task_params, plot=False)
-    model = Repr.fit()
-
-    # Reproductions for the same situations
-    traj, _ = Repr.reproduce(model, show_demo_idx=2)
-
-    # Reproductions for new situations
-    ref_demo_idx = 2
-    start_xdx = [Repr.demos_xdx[ref_demo_idx][0]]
-    end_xdx = [Repr.demos_xdx[ref_demo_idx][0]]
-    Repr.task_params = {'frame_origins': [start_xdx, end_xdx], 'frame_names': ['start', 'end']}
-    traj, _ = Repr.generate(model, ref_demo_idx)
-
-
-def test_7d_bi_tpgmm():
-    left_raw_demo = np.load('../examples/data/LFD_ML/LeftHand.npy')
-    right_raw_demo = np.load('../examples/data/LFD_ML/RightHand.npy')
-    demos_left_x = [left_raw_demo[500:635, :], left_raw_demo[635:770, :], left_raw_demo[770:905, :]]
-    demos_right_x = [right_raw_demo[500:635, :], right_raw_demo[635:770, :], right_raw_demo[770:905, :]]
-
-    # --- TP-GMMBi ---
-    # Define the task parameters
-    start_xdx_l = [demos_left_x[i][0] for i in range(len(demos_left_x))]  # TODO: change to xdx
-    end_xdx_l = [demos_left_x[i][-1] for i in range(len(demos_left_x))]
-    start_xdx_r = [demos_right_x[i][0] for i in range(len(demos_right_x))]
-    end_xdx_r = [demos_right_x[i][-1] for i in range(len(demos_right_x))]
-    task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
-                   'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
-    # Fit the model
-    Repr = rf.ml.TPGMMBi(demos_left_x, demos_right_x, task_params, plot=False)
-    model_l, model_r = Repr.fit()
-
-    # Reproductions for the same situations
-    traj_l, traj_r, _, _ = Repr.reproduce([model_l, model_r], show_demo_idx=2)
-
-    # Reproductions for new situations
-    ref_demo_idx = 2
-    start_xdx_l = [Repr.repr_l.demos_xdx[ref_demo_idx][0]]
-    end_xdx_l = [Repr.repr_l.demos_xdx[ref_demo_idx][0]]
-    start_xdx_r = [Repr.repr_r.demos_xdx[ref_demo_idx][0]]
-    end_xdx_r = [Repr.repr_r.demos_xdx[ref_demo_idx][0]]
-    Repr.task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
-                        'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
-    traj_l, traj_r, _, _ = Repr.generate([model_l, model_r], ref_demo_idx)
+# def test_7d_uni_tpgmm():
+#     raw_demo = np.load('../examples/data/LFD_ML/LeftHand.npy')
+#     demos_x = [raw_demo[500:635, :], raw_demo[635:770, :], raw_demo[770:905, :]]
+#
+#     start_xdx = [demos_x[i][0] for i in range(len(demos_x))]  # TODO: change to xdx
+#     end_xdx = [demos_x[i][-1] for i in range(len(demos_x))]
+#     task_params = {'frame_origins': [start_xdx, end_xdx], 'frame_names': ['start', 'end']}
+#     Repr = rf.RofuncML.TPGMM(demos_x, task_params, plot=False)
+#     model = Repr.fit()
+#
+#     # Reproductions for the same situations
+#     traj, _ = Repr.reproduce(model, show_demo_idx=2)
+#
+#     # Reproductions for new situations
+#     ref_demo_idx = 2
+#     start_xdx = [Repr.demos_xdx[ref_demo_idx][0]]
+#     end_xdx = [Repr.demos_xdx[ref_demo_idx][0]]
+#     Repr.task_params = {'frame_origins': [start_xdx, end_xdx], 'frame_names': ['start', 'end']}
+#     traj, _ = Repr.generate(model, ref_demo_idx)
+#
+#
+# def test_7d_bi_tpgmm():
+#     left_raw_demo = np.load('../examples/data/LFD_ML/LeftHand.npy')
+#     right_raw_demo = np.load('../examples/data/LFD_ML/RightHand.npy')
+#     demos_left_x = [left_raw_demo[500:635, :], left_raw_demo[635:770, :], left_raw_demo[770:905, :]]
+#     demos_right_x = [right_raw_demo[500:635, :], right_raw_demo[635:770, :], right_raw_demo[770:905, :]]
+#
+#     # --- TP-GMMBi ---
+#     # Define the task parameters
+#     start_xdx_l = [demos_left_x[i][0] for i in range(len(demos_left_x))]  # TODO: change to xdx
+#     end_xdx_l = [demos_left_x[i][-1] for i in range(len(demos_left_x))]
+#     start_xdx_r = [demos_right_x[i][0] for i in range(len(demos_right_x))]
+#     end_xdx_r = [demos_right_x[i][-1] for i in range(len(demos_right_x))]
+#     task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
+#                    'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
+#     # Fit the model
+#     Repr = rf.RofuncML.TPGMMBi(demos_left_x, demos_right_x, task_params, plot=False)
+#     model_l, model_r = Repr.fit()
+#
+#     # Reproductions for the same situations
+#     traj_l, traj_r, _, _ = Repr.reproduce([model_l, model_r], show_demo_idx=2)
+#
+#     # Reproductions for new situations
+#     ref_demo_idx = 2
+#     start_xdx_l = [Repr.repr_l.demos_xdx[ref_demo_idx][0]]
+#     end_xdx_l = [Repr.repr_l.demos_xdx[ref_demo_idx][0]]
+#     start_xdx_r = [Repr.repr_r.demos_xdx[ref_demo_idx][0]]
+#     end_xdx_r = [Repr.repr_r.demos_xdx[ref_demo_idx][0]]
+#     Repr.task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
+#                         'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
+#     traj_l, traj_r, _, _ = Repr.generate([model_l, model_r], ref_demo_idx)
 
 
 if __name__ == '__main__':

tests/test_tpgmm_bi.py

@@ -23,7 +23,7 @@ def test_bi_spatial_data():
     task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
                    'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
 
-    Repr = rf.ml.TPGMM_RPCtrl(demos_left_x, demos_right_x, task_params, plot=False)
+    Repr = rf.RofuncML.TPGMM_RPCtrl(demos_left_x, demos_right_x, task_params, plot=False)
     model_l, model_r, model_c = Repr.fit()
 
     Repr.reproduce([model_l, model_r, model_c], show_demo_idx=2)
@@ -54,7 +54,7 @@ def test_bi_temporal_data():
     task_params = {'left': {'frame_origins': [start_xdx_l, end_xdx_l], 'frame_names': ['start', 'end']},
                    'right': {'frame_origins': [start_xdx_r, end_xdx_r], 'frame_names': ['start', 'end']}}
 
-    Repr = rf.ml.TPGMM_RPCtrl(demos_left_x, demos_right_x, task_params, plot=False)
+    Repr = rf.RofuncML.TPGMM_RPCtrl(demos_left_x, demos_right_x, task_params, plot=False)
     model_l, model_r, model_c = Repr.fit()
 
     Repr.reproduce([model_l, model_r, model_c], show_demo_idx=2)

tests/test_tpgmr.py

@@ -12,7 +12,7 @@ def test_2d_uni_tpgmr():
     start_xdx = [demos_x[i][0] for i in range(len(demos_x))]  # TODO: change to xdx
     end_xdx = [demos_x[i][-1] for i in range(len(demos_x))]
     task_params = {'frame_origins': [start_xdx, end_xdx], 'frame_names': ['start', 'end']}
-    Repr = rf.ml.TPGMR(demos_x, task_params, plot=False)
+    Repr = rf.RofuncML.TPGMR(demos_x, task_params, plot=False)
     model = Repr.fit()
 
     traj, _ = Repr.reproduce(model, show_demo_idx=2)
@@ -27,7 +27,7 @@ def test_7d_uni_tpgmr():
     start_xdx = [demos_x[i][0] for i in range(len(demos_x))]  # TODO: change to xdx
     end_xdx = [demos_x[i][-1] for i in range(len(demos_x))]
     task_params = {'frame_origins': [start_xdx, end_xdx], 'frame_names': ['start', 'end']}
-    Repr = rf.ml.TPGMR(demos_x, task_params, plot=False)
+    Repr = rf.RofuncML.TPGMR(demos_x, task_params, plot=False)
     model = Repr.fit()
 
     # Reproductions for the same situations