Another distance metric about st_dbscan HOT 5 CLOSED

    def fit(self, X):
        """
        Apply the ST DBSCAN algorithm 
        ----------
        X : 2D numpy array with
            The first element of the array should be the time 
            attribute as float. The following positions in the array are 
            treated as spatial coordinates. The structure should look like this [[time_step1, x, y], [time_step2, x, y]..]
            For example 2D dataset:
            array([[0,0.45,0.43],
            [0,0.54,0.34],...])
        Returns
        -------
        self
        """
        # check if input is correct
        X = check_array(X)

        if not self.eps1 > 0.0 or not self.eps2 > 0.0 or not self.min_samples > 0.0:
            raise ValueError('eps1, eps2, minPts must be positive')

        n, m = X.shape

        # Compute sqaured form Euclidean Distance Matrix for 'time' attribute and the spatial attributes
        time_dist = pdist(X[:, 0].reshape(n, 1), metric=self.metric)

        # --------
        # --------
        # Line changed here:
        # np.array of weights 
        weights = np.array([0.5, 1, 0.2, 0.3]) # weights for the features 
        euc_dist = pdist(X[:, 1:], 'wminkowski', p=2, w=weights)
        # afterwards the same code snippets  
        # --------
        # --------

        # filter the euc_dist matrix using the time_dist
        dist = np.where(time_dist <= self.eps2, euc_dist, 2 * self.eps1)

        db = DBSCAN(eps=self.eps1,
                    min_samples=self.min_samples,
                    metric='precomputed')
        db.fit(squareform(dist))

        self.labels = db.labels_

        return self