Python Examples of numpy.vstack

Source File: BDA.py From transferlearning with MIT License

7 votes

def proxy_a_distance(source_X, target_X):
    """
    Compute the Proxy-A-Distance of a source/target representation
    """
    nb_source = np.shape(source_X)[0]
    nb_target = np.shape(target_X)[0]

    train_X = np.vstack((source_X, target_X))
    train_Y = np.hstack((np.zeros(nb_source, dtype=int),
                         np.ones(nb_target, dtype=int)))

    clf = svm.LinearSVC(random_state=0)
    clf.fit(train_X, train_Y)
    y_pred = clf.predict(train_X)
    error = metrics.mean_absolute_error(train_Y, y_pred)
    dist = 2 * (1 - 2 * error)
    return dist

Source File: tools_fri_doa_plane.py From FRIDA with MIT License

6 votes

def mtx_updated_G(phi_recon, M, mtx_amp2visi_ri, mtx_fri2visi_ri):
    """
    Update the linear transformation matrix that links the FRI sequence to the
    visibilities by using the reconstructed Dirac locations.
    :param phi_recon: the reconstructed Dirac locations (azimuths)
    :param M: the Fourier series expansion is between -M to M
    :param p_mic_x: a vector that contains microphones' x-coordinates
    :param p_mic_y: a vector that contains microphones' y-coordinates
    :param mtx_freq2visi: the linear mapping from Fourier series to visibilities
    :return:
    """
    L = 2 * M + 1
    ms_half = np.reshape(np.arange(-M, 1, step=1), (-1, 1), order='F')
    phi_recon = np.reshape(phi_recon, (1, -1), order='F')
    mtx_amp2freq = np.exp(-1j * ms_half * phi_recon)  # size: (M + 1) x K
    mtx_amp2freq_ri = np.vstack((mtx_amp2freq.real, mtx_amp2freq.imag[:-1, :]))  # size: (2M + 1) x K
    mtx_fri2amp_ri = linalg.lstsq(mtx_amp2freq_ri, np.eye(L))[0]
    # projection mtx_freq2visi to the null space of mtx_fri2amp
    mtx_null_proj = np.eye(L) - np.dot(mtx_fri2amp_ri.T,
                                       linalg.lstsq(mtx_fri2amp_ri.T, np.eye(L))[0])
    G_updated = np.dot(mtx_amp2visi_ri, mtx_fri2amp_ri) + \
                np.dot(mtx_fri2visi_ri, mtx_null_proj)
    return G_updated

Source File: TCA.py From transferlearning with MIT License

6 votes

def fit(self, Xs, Xt):
        '''
        Transform Xs and Xt
        :param Xs: ns * n_feature, source feature
        :param Xt: nt * n_feature, target feature
        :return: Xs_new and Xt_new after TCA
        '''
        X = np.hstack((Xs.T, Xt.T))
        X /= np.linalg.norm(X, axis=0)
        m, n = X.shape
        ns, nt = len(Xs), len(Xt)
        e = np.vstack((1 / ns * np.ones((ns, 1)), -1 / nt * np.ones((nt, 1))))
        M = e * e.T
        M = M / np.linalg.norm(M, 'fro')
        H = np.eye(n) - 1 / n * np.ones((n, n))
        K = kernel(self.kernel_type, X, None, gamma=self.gamma)
        n_eye = m if self.kernel_type == 'primal' else n
        a, b = np.linalg.multi_dot([K, M, K.T]) + self.lamb * np.eye(n_eye), np.linalg.multi_dot([K, H, K.T])
        w, V = scipy.linalg.eig(a, b)
        ind = np.argsort(w)
        A = V[:, ind[:self.dim]]
        Z = np.dot(A.T, K)
        Z /= np.linalg.norm(Z, axis=0)
        Xs_new, Xt_new = Z[:, :ns].T, Z[:, ns:].T
        return Xs_new, Xt_new

Source File: snippets.py From Collaborative-Learning-for-Weakly-Supervised-Object-Detection with MIT License

6 votes

def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)):
  """ A wrapper function to generate anchors given different scales
    Also return the number of anchors in variable 'length'
  """
  anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
  A = anchors.shape[0]
  shift_x = np.arange(0, width) * feat_stride
  shift_y = np.arange(0, height) * feat_stride
  shift_x, shift_y = np.meshgrid(shift_x, shift_y)
  shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
  K = shifts.shape[0]
  # width changes faster, so here it is H, W, C
  anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
  anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
  length = np.int32(anchors.shape[0])

  return anchors, length

Source File: coroOnlyScheduler.py From EXOSIMS with BSD 3-Clause "New" or "Revised" License

6 votes

def scheduleRevisit(self, sInd, smin, det, pInds):
        """A Helper Method for scheduling revisits after observation detection
        Args:
            sInd - sInd of the star just detected
            smin - minimum separation of the planet to star of planet just detected
            det - 
            pInds - Indices of planets around target star
        Return:
            updates self.starRevisit attribute
        """

        TK = self.TimeKeeping

        t_rev = TK.currentTimeNorm.copy() + self.revisit_wait[sInd]
        # finally, populate the revisit list (NOTE: sInd becomes a float)
        revisit = np.array([sInd, t_rev.to('day').value])
        if self.starRevisit.size == 0:#If starRevisit has nothing in it
            self.starRevisit = np.array([revisit])#initialize sterRevisit
        else:
            revInd = np.where(self.starRevisit[:,0] == sInd)[0]#indices of the first column of the starRevisit list containing sInd 
            if revInd.size == 0:
                self.starRevisit = np.vstack((self.starRevisit, revisit))
            else:
                self.starRevisit[revInd,1] = revisit[1]#over