def test_distance2(): with util_numpy.test_uses_numpy() as np: s = np.array([[0., 0, 1, 2, 1, 0, 1.3, 0, 0], [0., 0, 1, 2, 1, 0, 1, 0, 0], [0., 1, 2, 0, 0, 0, 0, 0, 0], [0., 1, 2, 0, 0, 0, 0, 0, 0], [1., 2, 0, 0, 0, 0, 0, 1, 1], [1., 2, 0, 0, 0, 0, 0, 1, 1], [1., 2, 0, 0, 1, 0, 0, 1, 1]]) l = np.array([1, 1, 1, 1, 0, 0, 0]) if directory: if not dtwvis.test_without_visualization(): plot_series(s, l) savefig = str(directory / "dts.dot") else: savefig = None prototypeidx = 0 ml_values, cl_values, clfs, importances = \ dtww.series_to_dt(s, l, prototypeidx, max_clfs=50, savefig=savefig) logger.debug(f"ml_values = {dict(ml_values)}") logger.debug(f"cl_values = {dict(cl_values)}") weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values, only_max=False, strict_cl=True) if not dtwvis.test_without_visualization(): if directory: plot_margins(s[prototypeidx], weights, clfs)
def test_distance5(): with util_numpy.test_uses_numpy() as np: s = np.array([ [0., 0, 0, 2, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 0 [0., 0, 2, 0, -2, 0, 2, 0, -2, 0, 2, 0, -2, 0, 0], # 1 [0., 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0] # 2 ]) l = np.array([1, 1, 0]) if directory: if not dtwvis.test_without_visualization(): plot_series(s, l) prototypeidx = 0 ml_values, cl_values, clf, importances = dtww.series_to_dt( s, l, prototypeidx, window=4) logger.debug(f"ml_values = {dict(ml_values)}") logger.debug(f"cl_values = {dict(cl_values)}") weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values, only_max=False, strict_cl=True) if directory: if not dtwvis.test_without_visualization(): plot_margins(s[prototypeidx], weights, clf)
def test_distance6(): with util_numpy.test_uses_numpy() as np: s = np.loadtxt(Path(__file__).parent / "rsrc" / "series_0.csv", delimiter=',') l = np.loadtxt(Path(__file__).parent / "rsrc" / "labels_0.csv", delimiter=',') if directory: if not dtwvis.test_without_visualization(): plot_series(s, l) savefig = str(directory / "dts.dot") else: savefig = None prototypeidx = 3 labels = np.zeros(l.shape) labels[l == l[prototypeidx]] = 1 ml_values, cl_values, clf, importances = \ dtww.series_to_dt(s, labels, prototypeidx, window=0, min_ig=0.1, savefig=savefig) logger.debug(f"ml_values = {dict(ml_values)}") logger.debug(f"cl_values = {dict(cl_values)}") weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values, only_max=False, strict_cl=True) if directory: if not dtwvis.test_without_visualization(): plot_margins(s[prototypeidx], weights, clf, prototypeidx)
def test_distance7(): s = np.array([[0.0, 0.3, 0.5, 0.8, 1.0, 0.1, 0.0, 0.1], [0.0, 0.2, 0.3, 0.7, 1.1, 0.0, 0.1, 0.0], [0.1, 0.0, 1.0, 1.0, 1.0, 0.9, 0.0, 0.0], [0.0, 0.0, 1.1, 0.9, 1.0, 1.0, 0.0, 0.0], [0.0, 0.1, 1.1, 1.0, 0.9, 0.9, 0.0, 0.0], [0.0, 0.1, 1.0, 1.1, 0.9, 1.0, 0.0, 0.1], [0.0, 0.1, 0.4, 0.3, 0.2, 0.3, 0.0, 0.0], [0.1, 0.0, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1]]) l = np.array([1, 1, 0, 0, 0, 0, 0, 0]) prototypeidx = 0 if directory: plot_series(s, l, prototypeidx) savefig = str(directory / "dts.dot") else: savefig = None ml_values, cl_values, clf, imp = dtww.series_to_dt( s, l, prototypeidx, window=0, min_ig=0.01, savefig=savefig, warping_paths_fnc=dtww.warping_paths) # logger.debug(f"ml_values = {dict(ml_values)}") # logger.debug(f"cl_values = {dict(cl_values)}") weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values, only_max=False, strict_cl=True) if directory: plot_margins(s[prototypeidx], weights, clf, imp)
def test_distance4(): s = np.array([ [0., 0, 1, 2, 1, 0, 1.3, 0, 0], # 0 [0., 1, 2, 0, 0, 0, 0, 0, 0], # 1 [1., 2, 0, 0, 0, 0, 0, 1, 1], # 2 [0., 0, 1, 2, 1, 0, 1, 0, 0], # 3 [0., 1, 2, 0, 0, 0, 0, 0, 0], # 4 [1., 2, 0, 0, 0, 0, 0, 1, 1], # 5 [1., 2, 0, 0, 1, 0, 0, 1, 1], # 6 [1., 2, 0.05, 0.01, 0.9, 0, 0, 1, 1] ]) # 7 l = np.array([1, 0, 0, 1, 0, 0, 0, 0]) if directory: plot_series(s, l) savefig = str(directory / "dts.dot") else: savefig = None prototypeidx = 0 ml_values, cl_values, clf, importances = \ dtww.series_to_dt(s, l, prototypeidx, window=2, min_ig=0.1, savefig=savefig) logger.debug(f"ml_values = {dict(ml_values)}") logger.debug(f"cl_values = {dict(cl_values)}") weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values, only_max=False, strict_cl=True) if directory: plot_margins(s[prototypeidx], weights, clf)
def test_distance2(directory=None): directory = prepare_directory(directory) s = np.array([[0., 0, 1, 2, 1, 0, 1.3, 0, 0], [0., 0, 1, 2, 1, 0, 1, 0, 0], [0., 1, 2, 0, 0, 0, 0, 0, 0], [0., 1, 2, 0, 0, 0, 0, 0, 0], [1., 2, 0, 0, 0, 0, 0, 1, 1], [1., 2, 0, 0, 0, 0, 0, 1, 1], [1., 2, 0, 0, 1, 0, 0, 1, 1]]) l = np.array([1, 1, 1, 1, 0, 0, 0]) if directory: plot_series(s, l) prototypeidx = 0 ml_values, cl_values, clfs = dtww.series_to_dt(s, l, prototypeidx, max_clfs=50, savefig=str(directory / "dts.dot")) logger.debug(f"ml_values = {dict(ml_values)}") logger.debug(f"cl_values = {dict(cl_values)}") weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values, only_max=False, strict_cl=True) if directory: plot_margins(s[prototypeidx], weights, clfs)
def test_distance6(directory=None): directory = prepare_directory(directory) s = np.loadtxt(Path(__file__).parent / "rsrc" / "series_0.csv", delimiter=',') l = np.loadtxt(Path(__file__).parent / "rsrc" / "labels_0.csv", delimiter=',') if directory: plot_series(s, l) prototypeidx = 3 labels = np.zeros(l.shape) labels[l == l[prototypeidx]] = 1 ml_values, cl_values, clf = dtww.series_to_dt(s, labels, prototypeidx, window=0, min_ig=0.1, savefig=str(directory / "dts.dot")) logger.debug(f"ml_values = {dict(ml_values)}") logger.debug(f"cl_values = {dict(cl_values)}") weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values, only_max=False, strict_cl=True) if directory: plot_margins(s[prototypeidx], weights, clf, prototypeidx)
def test_distance5(directory=None): directory = prepare_directory(directory) s = np.array([ [0., 0, 0, 2, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 0 [0., 0, 2, 0, -2, 0, 2, 0, -2, 0, 2, 0, -2, 0, 0], # 1 [0., 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0] # 2 ]) l = np.array([1, 1, 0]) if directory: plot_series(s, l) prototypeidx = 0 ml_values, cl_values, clf = dtww.series_to_dt(s, l, prototypeidx, window=4) logger.debug(f"ml_values = {dict(ml_values)}") logger.debug(f"cl_values = {dict(cl_values)}") weights = dtww.compute_weights_from_mlclvalues(s[prototypeidx], ml_values, cl_values, only_max=False, strict_cl=True) if directory: plot_margins(s[prototypeidx], weights, clf)