From 908679b72c215d1914d8e3956126fa44367b937f Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Fri, 15 Nov 2019 22:45:15 +0100
Subject: The big rename: sktda -> representations

---
 src/python/gudhi/representations/vector_methods.py | 485 +++++++++++++++++++++
 1 file changed, 485 insertions(+)
 create mode 100644 src/python/gudhi/representations/vector_methods.py

(limited to 'src/python/gudhi/representations/vector_methods.py')

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
new file mode 100644
index 00000000..bf32f18e
--- /dev/null
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -0,0 +1,485 @@
+# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
+# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
+# Author(s):       Mathieu Carrière
+#
+# Copyright (C) 2018-2019 Inria
+#
+# Modification(s):
+#   - YYYY/MM Author: Description of the modification
+
+import numpy as np
+from sklearn.base          import BaseEstimator, TransformerMixin
+from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler
+from sklearn.neighbors     import DistanceMetric
+
+from .preprocessing import DiagramScaler, BirthPersistenceTransform
+
+#############################################
+# Finite Vectorization methods ##############
+#############################################
+
+class PersistenceImage(BaseEstimator, TransformerMixin):
+    """
+    This is a class for computing persistence images from a list of persistence diagrams. A persistence image is a 2D function computed from a persistence diagram by convolving the diagram points with a weighted Gaussian kernel. The plane is then discretized into an image with pixels, which is flattened and returned as a vector. See http://jmlr.org/papers/v18/16-337.html for more details.
+    """
+    def __init__(self, bandwidth=1., weight=lambda x: 1, resolution=[20,20], im_range=[np.nan, np.nan, np.nan, np.nan]):
+        """
+        Constructor for the PersistenceImage class.
+
+        Parameters:
+            bandwidth (double): bandwidth of the Gaussian kernel (default 1.).
+            weight (function): weight function for the persistence diagram points (default constant function, ie lambda x: 1). This function must be defined on 2D points, ie lists or numpy arrays of the form [p_x,p_y].
+            resolution ([int,int]): size (in pixels) of the persistence image (default [20,20]).
+            im_range ([double,double,double,double]): minimum and maximum of each axis of the persistence image, of the form [x_min, x_max, y_min, y_max] (default [numpy.nan, numpy.nan, numpy.nan, numpyp.nan]). If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
+        """
+        self.bandwidth, self.weight = bandwidth, weight
+        self.resolution, self.im_range = resolution, im_range
+
+    def fit(self, X, y=None):
+        """
+        Fit the PersistenceImage class on a list of persistence diagrams: if any of the values in **im_range** is numpy.nan, replace it with the corresponding value computed on the given list of persistence diagrams.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+            y (n x 1 array): persistence diagram labels (unused).
+        """
+        if np.isnan(np.array(self.im_range)).any():
+            new_X = BirthPersistenceTransform().fit_transform(X)
+            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(new_X,y)
+            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+            self.im_range = np.where(np.isnan(np.array(self.im_range)), np.array([mx, Mx, my, My]), np.array(self.im_range))
+        return self
+
+    def transform(self, X):
+        """
+        Compute the persistence image for each persistence diagram individually and store the results in a single numpy array.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+    
+        Returns:
+            numpy array with shape (number of diagrams) x (number of pixels = **resolution[0]** x **resolution[1]**): output persistence images.
+        """
+        num_diag, Xfit = len(X), []
+        new_X = BirthPersistenceTransform().fit_transform(X)
+
+        for i in range(num_diag):
+
+            diagram, num_pts_in_diag = new_X[i], X[i].shape[0]
+
+            w = np.empty(num_pts_in_diag)
+            for j in range(num_pts_in_diag):
+                w[j] = self.weight(diagram[j,:])
+
+            x_values, y_values = np.linspace(self.im_range[0], self.im_range[1], self.resolution[0]), np.linspace(self.im_range[2], self.im_range[3], self.resolution[1])
+            Xs, Ys = np.tile((diagram[:,0][:,np.newaxis,np.newaxis]-x_values[np.newaxis,np.newaxis,:]),[1,self.resolution[1],1]), np.tile(diagram[:,1][:,np.newaxis,np.newaxis]-y_values[np.newaxis,:,np.newaxis],[1,1,self.resolution[0]])
+            image = np.tensordot(w, np.exp((-np.square(Xs)-np.square(Ys))/(2*np.square(self.bandwidth)))/(np.square(self.bandwidth)*2*np.pi), 1)
+
+            Xfit.append(image.flatten()[np.newaxis,:])
+
+        Xfit = np.concatenate(Xfit,0)
+
+        return Xfit
+
+class Landscape(BaseEstimator, TransformerMixin):
+    """
+    This is a class for computing persistence landscapes from a list of persistence diagrams. A persistence landscape is a collection of 1D piecewise-linear functions computed from the rank function associated to the persistence diagram. These piecewise-linear functions are then sampled uniformly on a given range and the corresponding vectors of samples are concatenated and returned. See http://jmlr.org/papers/v16/bubenik15a.html for more details.
+    """
+    def __init__(self, num_landscapes=5, resolution=100, sample_range=[np.nan, np.nan]):
+        """
+        Constructor for the Landscape class.
+
+        Parameters:
+            num_landscapes (int): number of piecewise-linear functions to output (default 5).
+            resolution (int): number of sample for all piecewise-linear functions (default 100).
+            sample_range ([double, double]): minimum and maximum of all piecewise-linear function domains, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn uniformly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
+        """
+        self.num_landscapes, self.resolution, self.sample_range = num_landscapes, resolution, sample_range
+
+    def fit(self, X, y=None):
+        """
+        Fit the Landscape class on a list of persistence diagrams: if any of the values in **sample_range** is numpy.nan, replace it with the corresponding value computed on the given list of persistence diagrams.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+            y (n x 1 array): persistence diagram labels (unused).
+        """
+        if np.isnan(np.array(self.sample_range)).any():
+            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        return self
+
+    def transform(self, X):
+        """
+        Compute the persistence landscape for each persistence diagram individually and concatenate the results.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+    
+        Returns:
+            numpy array with shape (number of diagrams) x (number of samples = **num_landscapes** x **resolution**): output persistence landscapes.
+        """
+        num_diag, Xfit = len(X), []
+        x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution)
+        step_x = x_values[1] - x_values[0]
+
+        for i in range(num_diag):
+
+            diagram, num_pts_in_diag = X[i], X[i].shape[0]
+
+            ls = np.zeros([self.num_landscapes, self.resolution])
+
+            events = []
+            for j in range(self.resolution):
+                events.append([])
+
+            for j in range(num_pts_in_diag):
+                [px,py] = diagram[j,:2]
+                min_idx = np.minimum(np.maximum(np.ceil((px          - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                mid_idx = np.minimum(np.maximum(np.ceil((0.5*(py+px) - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                max_idx = np.minimum(np.maximum(np.ceil((py          - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+
+                if min_idx < self.resolution and max_idx > 0:
+
+                    landscape_value = self.sample_range[0] + min_idx * step_x - px
+                    for k in range(min_idx, mid_idx):
+                        events[k].append(landscape_value)
+                        landscape_value += step_x
+
+                    landscape_value = py - self.sample_range[0] - mid_idx * step_x
+                    for k in range(mid_idx, max_idx):
+                        events[k].append(landscape_value)
+                        landscape_value -= step_x
+
+            for j in range(self.resolution):
+                events[j].sort(reverse=True)
+                for k in range( min(self.num_landscapes, len(events[j])) ):
+                    ls[k,j] = events[j][k]
+
+            Xfit.append(np.sqrt(2)*np.reshape(ls,[1,-1]))
+
+        Xfit = np.concatenate(Xfit,0)
+
+        return Xfit
+
+class Silhouette(BaseEstimator, TransformerMixin):
+    """
+    This is a class for computing persistence silhouettes from a list of persistence diagrams. A persistence silhouette is computed by taking a weighted average of the collection of 1D piecewise-linear functions given by the persistence landscapes, and then by uniformly sampling this average on a given range. Finally, the corresponding vector of samples is returned. See https://arxiv.org/abs/1312.0308 for more details.
+    """
+    def __init__(self, weight=lambda x: 1, resolution=100, sample_range=[np.nan, np.nan]):
+        """
+        Constructor for the Silhouette class.
+
+        Parameters:
+            weight (function): weight function for the persistence diagram points (default constant function, ie lambda x: 1). This function must be defined on 2D points, ie on lists or numpy arrays of the form [p_x,p_y].
+            resolution (int): number of samples for the weighted average (default 100).
+            sample_range ([double, double]): minimum and maximum for the weighted average domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn uniformly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
+        """
+        self.weight, self.resolution, self.sample_range = weight, resolution, sample_range
+
+    def fit(self, X, y=None):
+        """
+        Fit the Silhouette class on a list of persistence diagrams: if any of the values in **sample_range** is numpy.nan, replace it with the corresponding value computed on the given list of persistence diagrams.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+            y (n x 1 array): persistence diagram labels (unused).
+        """
+        if np.isnan(np.array(self.sample_range)).any():
+            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        return self
+
+    def transform(self, X):
+        """
+        Compute the persistence silhouette for each persistence diagram individually and concatenate the results.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+    
+        Returns:
+            numpy array with shape (number of diagrams) x (**resolution**): output persistence silhouettes.
+        """
+        num_diag, Xfit = len(X), []
+        x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution)
+        step_x = x_values[1] - x_values[0]
+
+        for i in range(num_diag):
+
+            diagram, num_pts_in_diag = X[i], X[i].shape[0]
+
+            sh, weights = np.zeros(self.resolution), np.zeros(num_pts_in_diag)
+            for j in range(num_pts_in_diag):
+                weights[j] = self.weight(diagram[j,:])
+            total_weight = np.sum(weights)
+
+            for j in range(num_pts_in_diag):
+
+                [px,py] = diagram[j,:2]
+                weight  = weights[j] / total_weight
+                min_idx = np.minimum(np.maximum(np.ceil((px          - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                mid_idx = np.minimum(np.maximum(np.ceil((0.5*(py+px) - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                max_idx = np.minimum(np.maximum(np.ceil((py          - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+
+                if min_idx < self.resolution and max_idx > 0:
+
+                    silhouette_value = self.sample_range[0] + min_idx * step_x - px
+                    for k in range(min_idx, mid_idx):
+                        sh[k] += weight * silhouette_value
+                        silhouette_value += step_x
+
+                    silhouette_value = py - self.sample_range[0] - mid_idx * step_x
+                    for k in range(mid_idx, max_idx):
+                        sh[k] += weight * silhouette_value
+                        silhouette_value -= step_x
+
+            Xfit.append(np.reshape(np.sqrt(2) * sh, [1,-1]))
+
+        Xfit = np.concatenate(Xfit, 0)
+
+        return Xfit 
+
+class BettiCurve(BaseEstimator, TransformerMixin):
+    """
+    This is a class for computing Betti curves from a list of persistence diagrams. A Betti curve is a 1D piecewise-constant function obtained from the rank function. It is sampled uniformly on a given range and the vector of samples is returned. See https://www.researchgate.net/publication/316604237_Time_Series_Classification_via_Topological_Data_Analysis for more details.
+    """
+    def __init__(self, resolution=100, sample_range=[np.nan, np.nan]):
+        """
+        Constructor for the BettiCurve class.
+
+        Parameters:
+            resolution (int): number of sample for the piecewise-constant function (default 100).
+            sample_range ([double, double]): minimum and maximum of the piecewise-constant function domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn uniformly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
+        """
+        self.resolution, self.sample_range = resolution, sample_range
+
+    def fit(self, X, y=None):
+        """
+        Fit the BettiCurve class on a list of persistence diagrams: if any of the values in **sample_range** is numpy.nan, replace it with the corresponding value computed on the given list of persistence diagrams.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+            y (n x 1 array): persistence diagram labels (unused).
+        """
+        if np.isnan(np.array(self.sample_range)).any():
+            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        return self
+
+    def transform(self, X):
+        """
+        Compute the Betti curve for each persistence diagram individually and concatenate the results.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+    
+        Returns:
+            numpy array with shape (number of diagrams) x (**resolution**): output Betti curves.
+        """
+        num_diag, Xfit = len(X), []
+        x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution)
+        step_x = x_values[1] - x_values[0]
+
+        for i in range(num_diag):
+
+            diagram, num_pts_in_diag = X[i], X[i].shape[0]
+
+            bc =  np.zeros(self.resolution)
+            for j in range(num_pts_in_diag):
+                [px,py] = diagram[j,:2]
+                min_idx = np.minimum(np.maximum(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                max_idx = np.minimum(np.maximum(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                for k in range(min_idx, max_idx):
+                    bc[k] += 1
+
+            Xfit.append(np.reshape(bc,[1,-1]))
+
+        Xfit = np.concatenate(Xfit, 0)
+
+        return Xfit
+
+class Entropy(BaseEstimator, TransformerMixin):
+    """
+    This is a class for computing persistence entropy. Persistence entropy is a statistic for persistence diagrams inspired from Shannon entropy. This statistic can also be used to compute a feature vector, called the entropy summary function. See https://arxiv.org/pdf/1803.08304.pdf for more details. Note that a previous implementation was contributed by Manuel Soriano-Trigueros.
+    """
+    def __init__(self, mode="scalar", normalized=True, resolution=100, sample_range=[np.nan, np.nan]):
+        """
+        Constructor for the Entropy class.
+
+        Parameters:
+            mode (string): what entropy to compute: either "scalar" for computing the entropy statistics, or "vector" for computing the entropy summary functions (default "scalar").
+            normalized (bool): whether to normalize the entropy summary function (default True). Used only if **mode** = "vector". 
+            resolution (int): number of sample for the entropy summary function (default 100). Used only if **mode** = "vector".
+            sample_range ([double, double]): minimum and maximum of the entropy summary function domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn uniformly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method. Used only if **mode** = "vector".
+        """
+        self.mode, self.normalized, self.resolution, self.sample_range = mode, normalized, resolution, sample_range
+
+    def fit(self, X, y=None):
+        """
+        Fit the Entropy class on a list of persistence diagrams.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+            y (n x 1 array): persistence diagram labels (unused).
+        """
+        if np.isnan(np.array(self.sample_range)).any():
+            pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
+            [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
+            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+        return self
+
+    def transform(self, X):
+        """
+        Compute the entropy for each persistence diagram individually and concatenate the results.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+    
+        Returns:
+            numpy array with shape (number of diagrams) x (1 if **mode** = "scalar" else **resolution**): output entropy.
+        """
+        num_diag, Xfit = len(X), []
+        x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution)
+        step_x = x_values[1] - x_values[0]
+        new_X = BirthPersistenceTransform().fit_transform(X)        
+
+        for i in range(num_diag):
+
+            orig_diagram, diagram, num_pts_in_diag = X[i], new_X[i], X[i].shape[0]
+            new_diagram = DiagramScaler(use=True, scalers=[([1], MaxAbsScaler())]).fit_transform([diagram])[0]
+
+            if self.mode == "scalar":
+                ent = - np.sum( np.multiply(new_diagram[:,1], np.log(new_diagram[:,1])) )
+                Xfit.append(np.array([[ent]]))
+
+            else:
+                ent = np.zeros(self.resolution)
+                for j in range(num_pts_in_diag):
+                    [px,py] = orig_diagram[j,:2]
+                    min_idx = np.minimum(np.maximum(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                    max_idx = np.minimum(np.maximum(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                    for k in range(min_idx, max_idx):
+                        ent[k] += (-1) * new_diagram[j,1] * np.log(new_diagram[j,1])
+                    if self.normalized:
+                        ent = ent / np.linalg.norm(ent, ord=1)
+                    Xfit.append(np.reshape(ent,[1,-1]))
+
+        Xfit = np.concatenate(Xfit, 0)
+
+        return Xfit
+
+class TopologicalVector(BaseEstimator, TransformerMixin):
+    """
+    This is a class for computing topological vectors from a list of persistence diagrams. The topological vector associated to a persistence diagram is the sorted vector of a slight modification of the pairwise distances between the persistence diagram points. See https://diglib.eg.org/handle/10.1111/cgf12692 for more details.
+    """
+    def __init__(self, threshold=10):
+        """
+        Constructor for the TopologicalVector class.
+
+        Parameters:
+            threshold (int): number of distances to keep (default 10). This is the dimension of the topological vector. If -1, this threshold is computed from the list of persistence diagrams by considering the one with the largest number of points and using the dimension of its corresponding topological vector as threshold. 
+        """
+        self.threshold = threshold
+
+    def fit(self, X, y=None):
+        """
+        Fit the TopologicalVector class on a list of persistence diagrams (this function actually does nothing but is useful when TopologicalVector is included in a scikit-learn Pipeline).
+
+        Parameters:
+            X (list of n x 2 or n x 1 numpy arrays): input persistence diagrams.
+            y (n x 1 array): persistence diagram labels (unused).
+        """
+        return self
+
+    def transform(self, X):
+        """
+        Compute the topological vector for each persistence diagram individually and concatenate the results.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+    
+        Returns:
+            numpy array with shape (number of diagrams) x (**threshold**): output topological vectors.
+        """
+        if self.threshold == -1:
+            thresh = np.array([X[i].shape[0] for i in range(len(X))]).max()
+        else:
+            thresh = self.threshold
+
+        num_diag = len(X)
+        Xfit = np.zeros([num_diag, thresh])
+
+        for i in range(num_diag):
+
+            diagram, num_pts_in_diag = X[i], X[i].shape[0]
+            pers = 0.5 * (diagram[:,1]-diagram[:,0])
+            min_pers = np.minimum(pers,np.transpose(pers))
+            distances = DistanceMetric.get_metric("chebyshev").pairwise(diagram)
+            vect = np.flip(np.sort(np.triu(np.minimum(distances, min_pers)), axis=None), 0)
+            dim = min(len(vect), thresh)
+            Xfit[i, :dim] = vect[:dim]
+
+        return Xfit
+
+class ComplexPolynomial(BaseEstimator, TransformerMixin):
+    """
+    This is a class for computing complex polynomials from a list of persistence diagrams. The persistence diagram points are seen as the roots of some complex polynomial, whose coefficients are returned in a complex vector. See https://link.springer.com/chapter/10.1007%2F978-3-319-23231-7_27 for more details.
+    """
+    def __init__(self, polynomial_type="R", threshold=10):
+        """
+        Constructor for the ComplexPolynomial class.
+
+        Parameters:
+           polynomial_type (char): either "R", "S" or "T" (default "R"). Type of complex polynomial that is going to be computed (explained in https://link.springer.com/chapter/10.1007%2F978-3-319-23231-7_27).
+           threshold (int): number of coefficients (default 10). This is the dimension of the complex vector of coefficients, i.e. the number of coefficients corresponding to the largest degree terms of the polynomial. If -1, this threshold is computed from the list of persistence diagrams by considering the one with the largest number of points and using the dimension of its corresponding complex vector of coefficients as threshold. 
+        """
+        self.threshold, self.polynomial_type = threshold, polynomial_type
+
+    def fit(self, X, y=None):
+        """
+        Fit the ComplexPolynomial class on a list of persistence diagrams (this function actually does nothing but is useful when ComplexPolynomial is included in a scikit-learn Pipeline).
+
+        Parameters:
+            X (list of n x 2 or n x 1 numpy arrays): input persistence diagrams.
+            y (n x 1 array): persistence diagram labels (unused).
+        """
+        return self
+
+    def transform(self, X):
+        """
+        Compute the complex vector of coefficients for each persistence diagram individually and concatenate the results.
+
+        Parameters:
+            X (list of n x 2 numpy arrays): input persistence diagrams.
+    
+        Returns:
+            numpy array with shape (number of diagrams) x (**threshold**): output complex vectors of coefficients.
+        """
+        if self.threshold == -1:
+            thresh = np.array([X[i].shape[0] for i in range(len(X))]).max()
+        else:
+            thresh = self.threshold
+
+        Xfit = np.zeros([len(X), thresh]) + 1j * np.zeros([len(X), thresh])
+        for d in range(len(X)):
+            D, N = X[d], X[d].shape[0]
+            if self.polynomial_type == "R":
+                roots = D[:,0] + 1j * D[:,1]
+            elif self.polynomial_type == "S":
+                alpha = np.linalg.norm(D, axis=1)
+                alpha = np.where(alpha==0, np.ones(N), alpha)
+                roots = np.multiply( np.multiply(  (D[:,0]+1j*D[:,1]), (D[:,1]-D[:,0])  ), 1./(np.sqrt(2)*alpha) )
+            elif self.polynomial_type == "T":
+                alpha = np.linalg.norm(D, axis=1)
+                roots = np.multiply(  (D[:,1]-D[:,0])/2, np.cos(alpha) - np.sin(alpha) + 1j * (np.cos(alpha) + np.sin(alpha))  )
+            coeff = [0] * (N+1)
+            coeff[N] = 1
+            for i in range(1, N+1): 
+                for j in range(N-i-1, N): 
+                    coeff[j] += ((-1) * roots[i-1] * coeff[j+1])   
+            coeff = np.array(coeff[::-1])[1:]
+            Xfit[d, :min(thresh, coeff.shape[0])] = coeff[:min(thresh, coeff.shape[0])]
+        return Xfit
-- 
cgit v1.2.3


From 7ea7538d02b8f0500dbc31f48dd31fb14d320135 Mon Sep 17 00:00:00 2001
From: Marc Glisse <marc.glisse@inria.fr>
Date: Wed, 20 Nov 2019 20:30:36 +0100
Subject: Minor tweaks to representations

uniform -> even
matmul -> indexing
min+max -> clip
---
 src/python/gudhi/representations/kernel_methods.py |  2 +-
 src/python/gudhi/representations/preprocessing.py  |  2 +-
 src/python/gudhi/representations/vector_methods.py | 34 +++++++++++-----------
 3 files changed, 19 insertions(+), 19 deletions(-)

(limited to 'src/python/gudhi/representations/vector_methods.py')

diff --git a/src/python/gudhi/representations/kernel_methods.py b/src/python/gudhi/representations/kernel_methods.py
index c855d2be..bfc83aff 100644
--- a/src/python/gudhi/representations/kernel_methods.py
+++ b/src/python/gudhi/representations/kernel_methods.py
@@ -161,7 +161,7 @@ class PersistenceScaleSpaceKernel(BaseEstimator, TransformerMixin):
         """
         Xp = list(X)
         for i in range(len(Xp)):
-            op_X = np.matmul(Xp[i], np.array([[0.,1.], [1.,0.]]))
+            op_X = Xp[i][:,[1,0]]
             Xp[i] = np.concatenate([Xp[i], op_X], axis=0)
         return self.pwg_.transform(Xp)
 
diff --git a/src/python/gudhi/representations/preprocessing.py b/src/python/gudhi/representations/preprocessing.py
index 83227ca1..487b5376 100644
--- a/src/python/gudhi/representations/preprocessing.py
+++ b/src/python/gudhi/representations/preprocessing.py
@@ -30,7 +30,7 @@ class BirthPersistenceTransform(BaseEstimator, TransformerMixin):
         Fit the BirthPersistenceTransform class on a list of persistence diagrams (this function actually does nothing but is useful when BirthPersistenceTransform is included in a scikit-learn Pipeline).
 
         Parameters:
-            X (n x 2 numpy array): input persistence diagrams.
+            X (list of n x 2 numpy array): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
         return self
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index bf32f18e..61c4fb84 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -83,7 +83,7 @@ class PersistenceImage(BaseEstimator, TransformerMixin):
 
 class Landscape(BaseEstimator, TransformerMixin):
     """
-    This is a class for computing persistence landscapes from a list of persistence diagrams. A persistence landscape is a collection of 1D piecewise-linear functions computed from the rank function associated to the persistence diagram. These piecewise-linear functions are then sampled uniformly on a given range and the corresponding vectors of samples are concatenated and returned. See http://jmlr.org/papers/v16/bubenik15a.html for more details.
+    This is a class for computing persistence landscapes from a list of persistence diagrams. A persistence landscape is a collection of 1D piecewise-linear functions computed from the rank function associated to the persistence diagram. These piecewise-linear functions are then sampled evenly on a given range and the corresponding vectors of samples are concatenated and returned. See http://jmlr.org/papers/v16/bubenik15a.html for more details.
     """
     def __init__(self, num_landscapes=5, resolution=100, sample_range=[np.nan, np.nan]):
         """
@@ -92,7 +92,7 @@ class Landscape(BaseEstimator, TransformerMixin):
         Parameters:
             num_landscapes (int): number of piecewise-linear functions to output (default 5).
             resolution (int): number of sample for all piecewise-linear functions (default 100).
-            sample_range ([double, double]): minimum and maximum of all piecewise-linear function domains, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn uniformly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
+            sample_range ([double, double]): minimum and maximum of all piecewise-linear function domains, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
         """
         self.num_landscapes, self.resolution, self.sample_range = num_landscapes, resolution, sample_range
 
@@ -136,9 +136,9 @@ class Landscape(BaseEstimator, TransformerMixin):
 
             for j in range(num_pts_in_diag):
                 [px,py] = diagram[j,:2]
-                min_idx = np.minimum(np.maximum(np.ceil((px          - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
-                mid_idx = np.minimum(np.maximum(np.ceil((0.5*(py+px) - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
-                max_idx = np.minimum(np.maximum(np.ceil((py          - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                min_idx = np.clip(np.ceil((px          - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
+                mid_idx = np.clip(np.ceil((0.5*(py+px) - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
+                max_idx = np.clip(np.ceil((py          - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
 
                 if min_idx < self.resolution and max_idx > 0:
 
@@ -165,7 +165,7 @@ class Landscape(BaseEstimator, TransformerMixin):
 
 class Silhouette(BaseEstimator, TransformerMixin):
     """
-    This is a class for computing persistence silhouettes from a list of persistence diagrams. A persistence silhouette is computed by taking a weighted average of the collection of 1D piecewise-linear functions given by the persistence landscapes, and then by uniformly sampling this average on a given range. Finally, the corresponding vector of samples is returned. See https://arxiv.org/abs/1312.0308 for more details.
+    This is a class for computing persistence silhouettes from a list of persistence diagrams. A persistence silhouette is computed by taking a weighted average of the collection of 1D piecewise-linear functions given by the persistence landscapes, and then by evenly sampling this average on a given range. Finally, the corresponding vector of samples is returned. See https://arxiv.org/abs/1312.0308 for more details.
     """
     def __init__(self, weight=lambda x: 1, resolution=100, sample_range=[np.nan, np.nan]):
         """
@@ -174,7 +174,7 @@ class Silhouette(BaseEstimator, TransformerMixin):
         Parameters:
             weight (function): weight function for the persistence diagram points (default constant function, ie lambda x: 1). This function must be defined on 2D points, ie on lists or numpy arrays of the form [p_x,p_y].
             resolution (int): number of samples for the weighted average (default 100).
-            sample_range ([double, double]): minimum and maximum for the weighted average domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn uniformly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
+            sample_range ([double, double]): minimum and maximum for the weighted average domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
         """
         self.weight, self.resolution, self.sample_range = weight, resolution, sample_range
 
@@ -219,9 +219,9 @@ class Silhouette(BaseEstimator, TransformerMixin):
 
                 [px,py] = diagram[j,:2]
                 weight  = weights[j] / total_weight
-                min_idx = np.minimum(np.maximum(np.ceil((px          - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
-                mid_idx = np.minimum(np.maximum(np.ceil((0.5*(py+px) - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
-                max_idx = np.minimum(np.maximum(np.ceil((py          - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                min_idx = np.clip(np.ceil((px          - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
+                mid_idx = np.clip(np.ceil((0.5*(py+px) - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
+                max_idx = np.clip(np.ceil((py          - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
 
                 if min_idx < self.resolution and max_idx > 0:
 
@@ -243,7 +243,7 @@ class Silhouette(BaseEstimator, TransformerMixin):
 
 class BettiCurve(BaseEstimator, TransformerMixin):
     """
-    This is a class for computing Betti curves from a list of persistence diagrams. A Betti curve is a 1D piecewise-constant function obtained from the rank function. It is sampled uniformly on a given range and the vector of samples is returned. See https://www.researchgate.net/publication/316604237_Time_Series_Classification_via_Topological_Data_Analysis for more details.
+    This is a class for computing Betti curves from a list of persistence diagrams. A Betti curve is a 1D piecewise-constant function obtained from the rank function. It is sampled evenly on a given range and the vector of samples is returned. See https://www.researchgate.net/publication/316604237_Time_Series_Classification_via_Topological_Data_Analysis for more details.
     """
     def __init__(self, resolution=100, sample_range=[np.nan, np.nan]):
         """
@@ -251,7 +251,7 @@ class BettiCurve(BaseEstimator, TransformerMixin):
 
         Parameters:
             resolution (int): number of sample for the piecewise-constant function (default 100).
-            sample_range ([double, double]): minimum and maximum of the piecewise-constant function domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn uniformly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
+            sample_range ([double, double]): minimum and maximum of the piecewise-constant function domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
         """
         self.resolution, self.sample_range = resolution, sample_range
 
@@ -290,8 +290,8 @@ class BettiCurve(BaseEstimator, TransformerMixin):
             bc =  np.zeros(self.resolution)
             for j in range(num_pts_in_diag):
                 [px,py] = diagram[j,:2]
-                min_idx = np.minimum(np.maximum(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
-                max_idx = np.minimum(np.maximum(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                min_idx = np.clip(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
+                max_idx = np.clip(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
                 for k in range(min_idx, max_idx):
                     bc[k] += 1
 
@@ -313,7 +313,7 @@ class Entropy(BaseEstimator, TransformerMixin):
             mode (string): what entropy to compute: either "scalar" for computing the entropy statistics, or "vector" for computing the entropy summary functions (default "scalar").
             normalized (bool): whether to normalize the entropy summary function (default True). Used only if **mode** = "vector". 
             resolution (int): number of sample for the entropy summary function (default 100). Used only if **mode** = "vector".
-            sample_range ([double, double]): minimum and maximum of the entropy summary function domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn uniformly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method. Used only if **mode** = "vector".
+            sample_range ([double, double]): minimum and maximum of the entropy summary function domain, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method. Used only if **mode** = "vector".
         """
         self.mode, self.normalized, self.resolution, self.sample_range = mode, normalized, resolution, sample_range
 
@@ -359,8 +359,8 @@ class Entropy(BaseEstimator, TransformerMixin):
                 ent = np.zeros(self.resolution)
                 for j in range(num_pts_in_diag):
                     [px,py] = orig_diagram[j,:2]
-                    min_idx = np.minimum(np.maximum(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
-                    max_idx = np.minimum(np.maximum(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0), self.resolution)
+                    min_idx = np.clip(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
+                    max_idx = np.clip(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
                     for k in range(min_idx, max_idx):
                         ent[k] += (-1) * new_diagram[j,1] * np.log(new_diagram[j,1])
                     if self.normalized:
-- 
cgit v1.2.3


From ce58cc97866605fe64df479e96d455e90f56f8e2 Mon Sep 17 00:00:00 2001
From: MathieuCarriere <mathieu.carriere3@gmail.com>
Date: Sun, 8 Dec 2019 21:22:09 -0500
Subject: fixed useless coordinates in Landscape if min and max are computed
 from data

---
 src/python/doc/representations.rst                 | 25 ++++++++++++++++++++--
 .../diagram_vectorizations_distances_kernels.py    |  6 +++---
 src/python/gudhi/representations/vector_methods.py | 12 ++++++++---
 3 files changed, 35 insertions(+), 8 deletions(-)

(limited to 'src/python/gudhi/representations/vector_methods.py')

diff --git a/src/python/doc/representations.rst b/src/python/doc/representations.rst
index b3131a25..b338f7f0 100644
--- a/src/python/doc/representations.rst
+++ b/src/python/doc/representations.rst
@@ -8,9 +8,9 @@ Representations manual
 
 .. include:: representations_sum.inc
 
-This module, originally named sklearn_tda, aims at bridging the gap between persistence diagrams and machine learning tools, in particular scikit-learn. It provides tools, using the scikit-learn standard interface, to compute distances and kernels on diagrams, and to convert diagrams into vectors.
+This module, originally available at https://github.com/MathieuCarriere/sklearn-tda and named sklearn_tda, aims at bridging the gap between persistence diagrams and machine learning, by providing implementations of most of the vector representations for persistence diagrams in the literature, in a scikit-learn format. More specifically, it provides tools, using the scikit-learn standard interface, to compute distances and kernels on persistence diagrams, and to convert these diagrams into vectors in Euclidean space.
 
-A diagram is represented as a numpy array of shape (n,2), as can be obtained from :func:`~gudhi.SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time.
+A diagram is represented as a numpy array of shape (n,2), as can be obtained from `SimplexTree.persistence_intervals_in_dimension` for instance. Points at infinity are represented as a numpy array of shape (n,1), storing only the birth time.
 
 A small example is provided
 
@@ -46,3 +46,24 @@ Metrics
    :members:
    :special-members:
    :show-inheritance:
+
+Basic example
+-------------
+
+This example computes the first two Landscapes associated to a persistence diagram with four points. The landscapes are evaluated on ten samples, leading to two vectors with ten coordinates each, that are eventually concatenated in order to produce a single vector representation.
+
+.. testcode::
+
+    import numpy as np
+    from gudhi.representations import Landscape
+    # A single diagram with 4 points
+    D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])
+    diags = [D]
+    l=Landscape(num_landscapes=2,resolution=10).fit_transform(diags)
+    print(l) 
+
+The output is:
+
+.. testoutput::
+
+    [[0.         1.25707872 2.51415744 1.88561808 0.7856742  2.04275292 3.29983165 2.51415744 1.25707872 0.         0.         0.         0.31426968 0.         0.62853936 0.         0.         0.31426968 1.25707872 0.        ]] 
diff --git a/src/python/example/diagram_vectorizations_distances_kernels.py b/src/python/example/diagram_vectorizations_distances_kernels.py
index 119072eb..f777984c 100755
--- a/src/python/example/diagram_vectorizations_distances_kernels.py
+++ b/src/python/example/diagram_vectorizations_distances_kernels.py
@@ -26,9 +26,9 @@ plt.show()
 
 LS = Landscape(resolution=1000)
 L = LS.fit_transform(diags)
-plt.plot(L[0][:1000])
-plt.plot(L[0][1000:2000])
-plt.plot(L[0][2000:3000])
+plt.plot(L[0][:999])
+plt.plot(L[0][999:2*999])
+plt.plot(L[0][2*999:3*999])
 plt.title("Landscape")
 plt.show()
 
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 61c4fb84..083551a4 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -104,10 +104,11 @@ class Landscape(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
+        self.nan_in_range = np.isnan(np.array(self.sample_range))
         if np.isnan(np.array(self.sample_range)).any():
             pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
             [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
-            self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range))
+            self.sample_range = np.where(self.nan_in_range, np.array([mx, My]), np.array(self.sample_range))
         return self
 
     def transform(self, X):
@@ -121,7 +122,7 @@ class Landscape(BaseEstimator, TransformerMixin):
             numpy array with shape (number of diagrams) x (number of samples = **num_landscapes** x **resolution**): output persistence landscapes.
         """
         num_diag, Xfit = len(X), []
-        x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution)
+        x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution + self.nan_in_range.sum())
         step_x = x_values[1] - x_values[0]
 
         for i in range(num_diag):
@@ -157,7 +158,12 @@ class Landscape(BaseEstimator, TransformerMixin):
                 for k in range( min(self.num_landscapes, len(events[j])) ):
                     ls[k,j] = events[j][k]
 
-            Xfit.append(np.sqrt(2)*np.reshape(ls,[1,-1]))
+            if self.nan_in_range[0]:
+                ls = ls[:,1:]
+            if self.nan_in_range[1]:
+                ls = ls[:,:-1]
+            ls = np.sqrt(2)*np.reshape(ls,[1,-1])
+            Xfit.append(ls)
 
         Xfit = np.concatenate(Xfit,0)
 
-- 
cgit v1.2.3


From 363ae171ee7f45cf11d01653e4d4e9580117cfd0 Mon Sep 17 00:00:00 2001
From: mathieu <mathieu.carriere3@gmail.com>
Date: Wed, 11 Dec 2019 13:50:21 -0500
Subject: fixed landscape

---
 .../example/diagram_vectorizations_distances_kernels.py    |  6 +++---
 src/python/gudhi/representations/vector_methods.py         | 14 +++++++-------
 2 files changed, 10 insertions(+), 10 deletions(-)

(limited to 'src/python/gudhi/representations/vector_methods.py')

diff --git a/src/python/example/diagram_vectorizations_distances_kernels.py b/src/python/example/diagram_vectorizations_distances_kernels.py
index 0ea4ba79..119072eb 100755
--- a/src/python/example/diagram_vectorizations_distances_kernels.py
+++ b/src/python/example/diagram_vectorizations_distances_kernels.py
@@ -26,9 +26,9 @@ plt.show()
 
 LS = Landscape(resolution=1000)
 L = LS.fit_transform(diags)
-plt.plot(L[0][:998])
-plt.plot(L[0][998:2*998])
-plt.plot(L[0][2*998:3*998])
+plt.plot(L[0][:1000])
+plt.plot(L[0][1000:2000])
+plt.plot(L[0][2000:3000])
 plt.title("Landscape")
 plt.show()
 
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 083551a4..cd532275 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -129,19 +129,19 @@ class Landscape(BaseEstimator, TransformerMixin):
 
             diagram, num_pts_in_diag = X[i], X[i].shape[0]
 
-            ls = np.zeros([self.num_landscapes, self.resolution])
+            ls = np.zeros([self.num_landscapes, self.resolution + self.nan_in_range.sum()])
 
             events = []
-            for j in range(self.resolution):
+            for j in range(self.resolution + self.nan_in_range.sum()):
                 events.append([])
 
             for j in range(num_pts_in_diag):
                 [px,py] = diagram[j,:2]
-                min_idx = np.clip(np.ceil((px          - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
-                mid_idx = np.clip(np.ceil((0.5*(py+px) - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
-                max_idx = np.clip(np.ceil((py          - self.sample_range[0]) / step_x).astype(int), 0, self.resolution)
+                min_idx = np.clip(np.ceil((px          - self.sample_range[0]) / step_x).astype(int), 0, self.resolution + self.nan_in_range.sum())
+                mid_idx = np.clip(np.ceil((0.5*(py+px) - self.sample_range[0]) / step_x).astype(int), 0, self.resolution + self.nan_in_range.sum())
+                max_idx = np.clip(np.ceil((py          - self.sample_range[0]) / step_x).astype(int), 0, self.resolution + self.nan_in_range.sum())
 
-                if min_idx < self.resolution and max_idx > 0:
+                if min_idx < self.resolution + self.nan_in_range.sum() and max_idx > 0:
 
                     landscape_value = self.sample_range[0] + min_idx * step_x - px
                     for k in range(min_idx, mid_idx):
@@ -153,7 +153,7 @@ class Landscape(BaseEstimator, TransformerMixin):
                         events[k].append(landscape_value)
                         landscape_value -= step_x
 
-            for j in range(self.resolution):
+            for j in range(self.resolution + self.nan_in_range.sum()):
                 events[j].sort(reverse=True)
                 for k in range( min(self.num_landscapes, len(events[j])) ):
                     ls[k,j] = events[j][k]
-- 
cgit v1.2.3


From 2886885ff4cf1f134863de0fa97b64f824d67622 Mon Sep 17 00:00:00 2001
From: mathieu <mathieu.carriere3@gmail.com>
Date: Wed, 11 Dec 2019 15:30:45 -0500
Subject: cleanup

---
 src/python/gudhi/representations/vector_methods.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'src/python/gudhi/representations/vector_methods.py')

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index cd532275..9b280f68 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -95,6 +95,7 @@ class Landscape(BaseEstimator, TransformerMixin):
             sample_range ([double, double]): minimum and maximum of all piecewise-linear function domains, of the form [x_min, x_max] (default [numpy.nan, numpy.nan]). It is the interval on which samples will be drawn evenly. If one of the values is numpy.nan, it can be computed from the persistence diagrams with the fit() method.
         """
         self.num_landscapes, self.resolution, self.sample_range = num_landscapes, resolution, sample_range
+        self.nan_in_range = np.isnan(np.array(self.sample_range))
 
     def fit(self, X, y=None):
         """
@@ -104,8 +105,7 @@ class Landscape(BaseEstimator, TransformerMixin):
             X (list of n x 2 numpy arrays): input persistence diagrams.
             y (n x 1 array): persistence diagram labels (unused).
         """
-        self.nan_in_range = np.isnan(np.array(self.sample_range))
-        if np.isnan(np.array(self.sample_range)).any():
+        if self.nan_in_range.any():
             pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y)
             [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]]
             self.sample_range = np.where(self.nan_in_range, np.array([mx, My]), np.array(self.sample_range))
-- 
cgit v1.2.3


From 7bd6907e577e22803fec179f652ecf0ec64dcb4a Mon Sep 17 00:00:00 2001
From: mathieu <mathieu.carriere3@gmail.com>
Date: Wed, 11 Dec 2019 15:38:00 -0500
Subject: cleanup for landscape resolution

---
 src/python/gudhi/representations/vector_methods.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

(limited to 'src/python/gudhi/representations/vector_methods.py')

diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py
index 9b280f68..fe26dbe2 100644
--- a/src/python/gudhi/representations/vector_methods.py
+++ b/src/python/gudhi/representations/vector_methods.py
@@ -96,6 +96,7 @@ class Landscape(BaseEstimator, TransformerMixin):
         """
         self.num_landscapes, self.resolution, self.sample_range = num_landscapes, resolution, sample_range
         self.nan_in_range = np.isnan(np.array(self.sample_range))
+        self.new_resolution = self.resolution + self.nan_in_range.sum()
 
     def fit(self, X, y=None):
         """
@@ -122,26 +123,26 @@ class Landscape(BaseEstimator, TransformerMixin):
             numpy array with shape (number of diagrams) x (number of samples = **num_landscapes** x **resolution**): output persistence landscapes.
         """
         num_diag, Xfit = len(X), []
-        x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution + self.nan_in_range.sum())
+        x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.new_resolution)
         step_x = x_values[1] - x_values[0]
 
         for i in range(num_diag):
 
             diagram, num_pts_in_diag = X[i], X[i].shape[0]
 
-            ls = np.zeros([self.num_landscapes, self.resolution + self.nan_in_range.sum()])
+            ls = np.zeros([self.num_landscapes, self.new_resolution])
 
             events = []
-            for j in range(self.resolution + self.nan_in_range.sum()):
+            for j in range(self.new_resolution):
                 events.append([])
 
             for j in range(num_pts_in_diag):
                 [px,py] = diagram[j,:2]
-                min_idx = np.clip(np.ceil((px          - self.sample_range[0]) / step_x).astype(int), 0, self.resolution + self.nan_in_range.sum())
-                mid_idx = np.clip(np.ceil((0.5*(py+px) - self.sample_range[0]) / step_x).astype(int), 0, self.resolution + self.nan_in_range.sum())
-                max_idx = np.clip(np.ceil((py          - self.sample_range[0]) / step_x).astype(int), 0, self.resolution + self.nan_in_range.sum())
+                min_idx = np.clip(np.ceil((px          - self.sample_range[0]) / step_x).astype(int), 0, self.new_resolution)
+                mid_idx = np.clip(np.ceil((0.5*(py+px) - self.sample_range[0]) / step_x).astype(int), 0, self.new_resolution)
+                max_idx = np.clip(np.ceil((py          - self.sample_range[0]) / step_x).astype(int), 0, self.new_resolution)
 
-                if min_idx < self.resolution + self.nan_in_range.sum() and max_idx > 0:
+                if min_idx < self.new_resolution and max_idx > 0:
 
                     landscape_value = self.sample_range[0] + min_idx * step_x - px
                     for k in range(min_idx, mid_idx):
@@ -153,7 +154,7 @@ class Landscape(BaseEstimator, TransformerMixin):
                         events[k].append(landscape_value)
                         landscape_value -= step_x
 
-            for j in range(self.resolution + self.nan_in_range.sum()):
+            for j in range(self.new_resolution):
                 events[j].sort(reverse=True)
                 for k in range( min(self.num_landscapes, len(events[j])) ):
                     ls[k,j] = events[j][k]
-- 
cgit v1.2.3