From 9e1d74f44473deb1f4766329bb0d1c8af4dfdd73 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Fri, 21 Jun 2019 18:27:42 +0200 Subject: Started documenting --- ot/lp/__init__.py | 77 +++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 75 insertions(+), 2 deletions(-) (limited to 'ot') diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index decff29..e9635a1 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -313,10 +313,83 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None return X -def emd_1d(a, b, x_a, x_b, metric='sqeuclidean', dense=True, log=False): +def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): """Solves the Earth Movers distance problem between 1d measures and returns the OT matrix + + .. math:: + \gamma = arg\min_\gamma \sum_i \sum_j \gamma_{ij} d(x_a[i], x_b[j]) + + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma\geq 0 + where : + + - d is the metric + - x_a and x_b are the samples + - a and b are the sample weights + + Uses the algorithm proposed in [1]_ + + Parameters + ---------- + x_a : (ns,) or (ns, 1) ndarray, float64 + Source histogram (uniform weight if empty list) + x_b : (nt,) or (ns, 1) ndarray, float64 + Target histogram (uniform weight if empty list) + a : (ns,) ndarray, float64 + Source histogram (uniform weight if empty list) + b : (nt,) ndarray, float64 + Target histogram (uniform weight if empty list) + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. + Due to implementation details, this function runs faster when + dense is set to False. + metric: str, optional (default='sqeuclidean') + Metric to be used. Has to be a string. + Due to implementation details, this function runs faster when + `'sqeuclidean'` or `'euclidean'` metrics are used. + log: boolean, optional (default=False) + If True, returns a dictionary containing the cost. + Otherwise returns only the optimal transportation matrix. + + Returns + ------- + gamma: (ns, nt) ndarray + Optimal transportation matrix for the given parameters + log: dict + If input log is True, a dictionary containing the cost + + + Examples + -------- + + Simple example with obvious solution. The function emd_1d accepts lists and + perform automatic conversion to numpy arrays + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> x_a = [0., 2.] + >>> x_b = [0., 3.] + >>> ot.emd_1d(a, b, x_a, x_b) + array([[ 0.5, 0. ], + [ 0. , 0.5]]) + + References + ---------- + + .. [1] TODO + + See Also + -------- + ot.lp.emd : EMD for multidimensional distributions + ot.lp.emd2_1d : EMD for 1d distributions (returns cost instead of the + transportation matrix) + """ a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) @@ -353,7 +426,7 @@ def emd_1d(a, b, x_a, x_b, metric='sqeuclidean', dense=True, log=False): return G -def emd2_1d(a, b, x_a, x_b, metric='sqeuclidean', dense=True, log=False): +def emd2_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): """Solves the Earth Movers distance problem between 1d measures and returns the loss -- cgit v1.2.3