Python numpy.vstack方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
def image_reslice(image, spec, method=None, fill=0, dtype=None, weights=None, image_type=None):
    '''
    image_reslice(image, spec) yields a duplicate of the given image resliced to have the voxels
      indicated by the given image spec. Note that spec may be an image itself.

    Optional arguments that can be passed to image_interpolate() (asside from affine) are allowed
    here and are passed through.
    '''
    if image_type is None and is_image(image): image_type = to_image_type(image)
    spec = to_image_spec(spec)
    image = to_image(image)
    # we make a big mesh and interpolate at these points...
    imsh = spec['image_shape']
    (args, kw) = ([np.arange(n) for n in imsh[:3]], {'indexing': 'ij'})
    ijk = np.asarray([u.flatten() for u in np.meshgrid(*args, **kw)])
    ijk = np.dot(spec['affine'], np.vstack([ijk, np.ones([1,ijk.shape[1]])]))[:3]
    # interpolate here...
    u = image_interpolate(image, ijk, method=method, fill=fill, dtype=dtype, weights=weights)
    return to_image((np.reshape(u, imsh), spec), image_type=image_type) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
def angle_to_cortex(self, theta, rho):
        'See help(neuropythy.registration.RetinotopyModel.angle_to_cortex).'
        #TODO: This should be made to work correctly with visual area boundaries: this could be done
        # by, for each area (e.g., V2) looking at its boundaries (with V1 and V3) and flipping the
        # adjacent triangles so that there is complete coverage of each hemifield, guaranteed.
        if not pimms.is_vector(theta): return self.angle_to_cortex([theta], [rho])[0]
        theta = np.asarray(theta)
        rho = np.asarray(rho)
        zs = np.asarray(
            rho * np.exp([np.complex(z) for z in 1j * ((90.0 - theta)/180.0*np.pi)]),
            dtype=np.complex)
        coords = np.asarray([zs.real, zs.imag]).T
        if coords.shape[0] == 0: return np.zeros((0, len(self.visual_meshes), 2))
        # we step through each area in the forward model and return the appropriate values
        tx = self.transform
        res = np.transpose(
            [self.visual_meshes[area].interpolate(coords, 'cortical_coordinates', method='linear')
             for area in sorted(self.visual_meshes.keys())],
            (1,0,2))
        if tx is not None:
            res = np.asarray(
                [np.dot(tx, np.vstack((area_xy.T, np.ones(len(area_xy)))))[0:2].T
                 for area_xy in res])
        return res 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
def apply_affine(aff, coords):
    '''
    apply_affine(affine, coords) yields the result of applying the given affine transformation to
      the given coordinate or coordinates.

    This function expects coords to be a (dims X n) matrix but if the first dimension is neither 2
    nor 3, coords.T is used; i.e.:
      apply_affine(affine3x3, coords2xN) ==> newcoords2xN
      apply_affine(affine4x4, coords3xN) ==> newcoords3xN
      apply_affine(affine3x3, coordsNx2) ==> newcoordsNx2 (for N != 2)
      apply_affine(affine4x4, coordsNx3) ==> newcoordsNx3 (for N != 3)
    '''
    if aff is None: return coords
    (coords,tr) = (np.asanyarray(coords), False)
    if len(coords.shape) == 1: return np.squeeze(apply_affine(np.reshape(coords, (-1,1)), aff))
    elif len(coords.shape) > 2: raise ValueError('cannot apply affine to ND-array for N > 2')
    if   len(coords) == 2: aff = to_affine(aff, 2)
    elif len(coords) == 3: aff = to_affine(aff, 3)
    else: (coords,aff,tr) = (coords.T, to_affine(aff, coords.shape[1]), True)
    r = np.dot(aff, np.vstack([coords, np.ones([1,coords.shape[1]])]))[:-1]
    return r.T if tr else r 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
def subcurve(self, t0, t1):
        '''
        curve.subcurve(t0, t1) yields a curve-spline object that is equivalent to the given
          curve but that extends from curve(t0) to curve(t1) only.
        '''
        # if t1 is less than t0, then we want to actually do this in reverse...
        if t1 == t0: raise ValueError('Cannot take subcurve of a point')
        if t1 < t0:
            tt = self.curve_length()
            return self.reverse().subcurve(tt - t0, tt - t1)
        idx = [ii for (ii,t) in enumerate(self.t) if t0 < t and t < t1]
        pt0 = self(t0)
        pt1 = self(t1)
        coords = np.vstack([[pt0], self.coordinates.T[idx], [pt1]])
        ts = np.concatenate([[t0], self.t[idx], [t1]])
        dists  = None if self.distances is None else np.diff(ts)
        return CurveSpline(
            coords.T,
            order=self.order,
            smoothing=self.smoothing,
            periodic=False,
            distances=dists,
            meta_data=self.meta_data) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
def breastcancer_cont(replication=2):
    f = open(path + "breast_cancer_wisconsin_cont.txt", "r")
    data = np.loadtxt(f, delimiter=",", dtype=np.string0)
    x_train = np.array(data[:, range(0, 9)])
    y_train = np.array(data[:, 9])
    for j in range(replication - 1):
        x_train = np.vstack([x_train, data[:, range(0, 9)]])
        y_train = np.hstack([y_train, data[:, 9]])
    x_train = np.array(x_train, dtype=np.float)

    f = open(path + "breast_cancer_wisconsin_cont_test.txt")
    data = np.loadtxt(f, delimiter=",", dtype=np.string0)
    x_test = np.array(data[:, range(0, 9)])
    y_test = np.array(data[:, 9])
    for j in range(replication - 1):
        x_test = np.vstack([x_test, data[:, range(0, 9)]])
        y_test = np.hstack([y_test, data[:, 9]])
    x_test = np.array(x_test, dtype=np.float)

    return x_train, y_train, x_test, y_test 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
def breastcancer_disc(replication=2):
    f = open(path + "breast_cancer_wisconsin_disc.txt")
    data = np.loadtxt(f, delimiter=",")
    x_train = data[:, range(1, 10)]
    y_train = data[:, 10]
    for j in range(replication - 1):
        x_train = np.vstack([x_train, data[:, range(1, 10)]])
        y_train = np.hstack([y_train, data[:, 10]])

    f = open(path + "breast_cancer_wisconsin_disc_test.txt")
    data = np.loadtxt(f, delimiter=",")
    x_test = data[:, range(1, 10)]
    y_test = data[:, 10]
    for j in range(replication - 1):
        x_test = np.vstack([x_test, data[:, range(1, 10)]])
        y_test = np.hstack([y_test, data[:, 10]])

    return x_train, y_train, x_test, y_test 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
def iris(replication=2):
    f = open(path + "iris.txt")
    data = np.loadtxt(f, delimiter=",", dtype=np.string0)
    x_train = np.array(data[:, range(0, 4)], dtype=np.float)
    y_train = data[:, 4]

    for j in range(replication - 1):
        x_train = np.vstack([x_train, data[:, range(0, 4)]])
        y_train = np.hstack([y_train, data[:, 4]])
    x_train = np.array(x_train, dtype=np.float)

    f = open(path + "iris_test.txt")
    data = np.loadtxt(f, delimiter=",", dtype=np.string0)
    x_test = np.array(data[:, range(0, 4)], dtype=np.float)
    y_test = data[:, 4]

    for j in range(replication - 1):
        x_test = np.vstack([x_test, data[:, range(0, 4)]])
        y_test = np.hstack([y_test, data[:, 4]])
    x_test = np.array(x_test, dtype=np.float)

    return x_train, y_train, x_test, y_test 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
def regression_data():
    f = open(path + "regression_data1.txt")
    data = np.loadtxt(f, delimiter=",")
    x1 = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1)
    y1 = data[:, 1]
    f = open(path + "regression_data2.txt")
    data = np.loadtxt(f, delimiter=",")
    x2 = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1)
    y2 = data[:, 1]
    x1 = np.vstack((x1, x2))
    y1 = np.hstack((y1, y2))

    f = open(path + "regression_data_test1.txt")
    data = np.loadtxt(f, delimiter=",")
    x1_test = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1)
    y1_test = data[:, 1]
    f = open(path + "regression_data_test2.txt")
    data = np.loadtxt(f, delimiter=",")
    x2_test = np.insert(data[:, 0].reshape(len(data), 1), 0, np.ones(len(data)), axis=1)
    y2_test = data[:, 1]
    x1_test = np.vstack((x1_test, x2_test))
    y1_test = np.hstack((y1_test, y2_test))
    return x1, y1, x1_test, y1_test 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
def ex3(replication=2):
    f = open(path + "ex3.txt")
    train_data = np.loadtxt(f, delimiter=",")
    f = open(path + "ex3_test.txt")
    test_data = np.loadtxt(f, delimiter=",")

    x_train = np.insert(train_data[:, (0, 1)], 0, np.ones(len(train_data)), axis=1)
    y_train = train_data[:, 2]
    x_test = np.insert(test_data[:, (0, 1)], 0, np.ones(len(test_data)), axis=1)
    y_test = test_data[:, 2]

    for i in range(replication - 1):
        x_train = np.vstack((x_train, np.insert(train_data[:, (0, 1)], 0, np.ones(len(train_data)), axis=1)))
        y_train = np.hstack((y_train, train_data[:, 2]))

        x_test = np.vstack((x_test, np.insert(test_data[:, (0, 1)], 0, np.ones(len(test_data)), axis=1)))
        y_test = np.hstack((y_test, test_data[:, 2]))

    return x_train, y_train, x_test, y_test 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
def getGFKDim(Xs, Xt):
    Pss = PCA().fit(Xs).components_.T
    Pts = PCA().fit(Xt).components_.T
    Psstt = PCA().fit(np.vstack((Xs, Xt))).components_.T
    
    DIM = round(Xs.shape[1]*0.5)
    res = -1
    
    for d in range(1, DIM+1):
        Ps = Pss[:, :d]
        Pt = Pts[:, :d]
        Pst = Psstt[:, :d]
        alpha1 = getAngle(Ps, Pst, d)
        alpha2 = getAngle(Pt, Pst, d)
        D = (alpha1 + alpha2) * 0.5
        check = [round(D[1, dd]*100) == 100 for dd in range(d)]
        if True in check:
            res = list(map(lambda i: i == True, check)).index(True) 
            return res 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import vstack [as 別名]
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