Python numpy.ones方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def add_intercept(self, X):
        """Add 1's to data as last features."""
        # Data shape
        N, D = X.shape

        # Check if there's not already an intercept column
        if np.any(np.sum(X, axis=0) == N):

            # Report
            print('Intercept is not the last feature. Swapping..')

            # Find which column contains the intercept
            intercept_index = np.argwhere(np.sum(X, axis=0) == N)

            # Swap intercept to last
            X = X[:, np.setdiff1d(np.arange(D), intercept_index)]

        # Add intercept as last column
        X = np.hstack((X, np.ones((N, 1))))

        # Append column of 1's to data, and increment dimensionality
        return X, D+1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def test_one_hot():
    """Check if one_hot returns correct label matrices."""
    # Generate label vector
    y = np.hstack((np.ones((10,))*0,
                   np.ones((10,))*1,
                   np.ones((10,))*2))

    # Map to matrix
    Y, labels = one_hot(y)

    # Check for only 0's and 1's
    assert len(np.setdiff1d(np.unique(Y), [0, 1])) == 0

    # Check for correct labels
    assert np.all(labels == np.unique(y))

    # Check correct shape of matrix
    assert Y.shape[0] == y.shape[0]
    assert Y.shape[1] == len(labels) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def test_cross_phase_2d(self, dask):
        Ny, Nx = (32, 16)
        x = np.linspace(0, 1, num=Nx, endpoint=False)
        y = np.ones(Ny)
        f = 6
        phase_offset = np.pi/2
        signal1 = np.cos(2*np.pi*f*x)  # frequency = 1/(2*pi)
        signal2 = np.cos(2*np.pi*f*x - phase_offset)
        da1 = xr.DataArray(data=signal1*y[:,np.newaxis], name='a',
                          dims=['y','x'], coords={'y':y, 'x':x})
        da2 = xr.DataArray(data=signal2*y[:,np.newaxis], name='b',
                          dims=['y','x'], coords={'y':y, 'x':x})
        with pytest.raises(ValueError):
            xrft.cross_phase(da1, da2, dim=['y','x'])

        if dask:
            da1 = da1.chunk({'x': 16})
            da2 = da2.chunk({'x': 16})
        cp = xrft.cross_phase(da1, da2, dim=['x'])
        actual_phase_offset = cp.sel(freq_x=f).values
        npt.assert_almost_equal(actual_phase_offset, phase_offset) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def synthetic_field_xr(N, dL, amp, s,
                    other_dim_sizes=None, dim_order=True,
                    chunks=None):

    theta = xr.DataArray(synthetic_field(N, dL, amp, s),
                        dims=['y', 'x'],
                        coords={'y':range(N), 'x':range(N)}
                        )

    if other_dim_sizes:
        _da = xr.DataArray(np.ones(other_dim_sizes),
                           dims=['d%d'%i for i in range(len(other_dim_sizes))])
        if dim_order:
            theta = theta + _da
        else:
            theta = _da + theta

    if chunks:
        theta = theta.chunk(chunks)

    return theta 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def classical_mds(self, D):
        ''' 
        Classical multidimensional scaling

        Parameters
        ----------
        D : square 2D ndarray
            Euclidean Distance Matrix (matrix containing squared distances between points
        '''

        # Apply MDS algorithm for denoising
        n = D.shape[0]
        J = np.eye(n) - np.ones((n,n))/float(n)
        G = -0.5*np.dot(J, np.dot(D, J))

        s, U = np.linalg.eig(G)

        # we need to sort the eigenvalues in decreasing order
        s = np.real(s)
        o = np.argsort(s)
        s = s[o[::-1]]
        U = U[:,o[::-1]]

        S = np.diag(s)[0:self.dim,:]
        self.X = np.dot(np.sqrt(S),U.T) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def wrap_variable(self, var):
        """wrap layer.w into variables"""
        val = self.lay.w.get(var, None)
        if val is None:
            shape = self.lay.wshape[var]
            args = [0., 1e-2, shape]
            if 'moving_mean' in var:
                val = np.zeros(shape)
            elif 'moving_variance' in var:
                val = np.ones(shape)
            else:
                val = np.random.normal(*args)
            self.lay.w[var] = val.astype(np.float32)
            self.act = 'Init '
        if not self.var: return

        val = self.lay.w[var]
        self.lay.w[var] = tf.constant_initializer(val)
        if var in self._SLIM: return
        with tf.variable_scope(self.scope):
            self.lay.w[var] = tf.get_variable(var,
                shape = self.lay.wshape[var],
                dtype = tf.float32,
                initializer = self.lay.w[var]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def _add_default_meta_keys(self, results):
        """Add default meta keys.

        We set default meta keys including `pad_shape`, `scale_factor` and
        `img_norm_cfg` to avoid the case where no `Resize`, `Normalize` and
        `Pad` are implemented during the whole pipeline.

        Args:
            results (dict): Result dict contains the data to convert.

        Returns:
            results (dict): Updated result dict contains the data to convert.
        """
        img = results['img']
        results.setdefault('pad_shape', img.shape)
        results.setdefault('scale_factor', 1.0)
        num_channels = 1 if len(img.shape) < 3 else img.shape[2]
        results.setdefault(
            'img_norm_cfg',
            dict(
                mean=np.zeros(num_channels, dtype=np.float32),
                std=np.ones(num_channels, dtype=np.float32),
                to_rgb=False))
        return results 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def __init__(self, wave_len=254, wave_dif=64, buffer_size=5, loop_num=5, window=np.hanning(254)):
        self.wave_len = wave_len
        self.wave_dif = wave_dif
        self.buffer_size = buffer_size
        self.loop_num = loop_num
        self.window = window

        self.wave_buf = np.zeros(wave_len+wave_dif, dtype=float)
        self.overwrap_buf = np.zeros(wave_dif*buffer_size+(wave_len-wave_dif), dtype=float)
        self.spectrum_buffer = np.ones((self.buffer_size, self.wave_len), dtype=complex)
        self.absolute_buffer = np.ones((self.buffer_size, self.wave_len), dtype=complex)
        
        self.phase = np.zeros(self.wave_len, dtype=complex)
        self.phase += np.random.random(self.wave_len)-0.5 + np.random.random(self.wave_len)*1j - 0.5j
        self.phase[self.phase == 0] = 1
        self.phase /= np.abs(self.phase) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def predict_all(X, all_theta):
    rows = X.shape[0]
    params = X.shape[1]
    num_labels = all_theta.shape[0]
    
    # same as before, insert ones to match the shape
    X = np.insert(X, 0, values=np.ones(rows), axis=1)
    
    # convert to matrices
    X = np.matrix(X)
    all_theta = np.matrix(all_theta)
    
    # compute the class probability for each class on each training instance
    h = sigmoid(X * all_theta.T)
    
    # create array of the index with the maximum probability
    h_argmax = np.argmax(h, axis=1)
    
    # because our array was zero-indexed we need to add one for the true label prediction
    h_argmax = h_argmax + 1
    
    return h_argmax 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def prepare_poly_data(*args, power):
    """
    args: keep feeding in X, Xval, or Xtest
        will return in the same order
    """
    def prepare(x):
        # expand feature
        df = poly_features(x, power=power)

        # normalization
        ndarr = normalize_feature(df).as_matrix()

        # add intercept term
        return np.insert(ndarr, 0, np.ones(ndarr.shape[0]), axis=1)

    return [prepare(x) for x in args] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def create_sprite_image(images):
    if isinstance(images, list):
        images = np.array(images)
    img_h = images.shape[1]
    img_w = images.shape[2]
    # sprite 可以理解為所有小圖片拚成的大正方形矩陣
    m = int(np.ceil(np.sqrt(images.shape[0])))

    # 使用全 1 來初始化最終的大圖片
    sprite_image = np.ones((img_h*m, img_w*m))

    for i in range(m):
        for j in range(m):
            # 計算當前圖片編號
            cur = i * m + j
            if cur < images.shape[0]:
                # 將小圖片的內容複製到最終的 sprite 圖像
                sprite_image[i*img_h:(i+1)*img_h,
                             j*img_w:(j+1)*img_w] = images[cur]
    return sprite_image

# 加載 mnist 數據，製定 one_hot=False，得到的 labels 就是一個數字，而不是一個向量 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [as 別名]
def color_overlap(color1, *args):
    '''
    color_overlap(color1, color2...) yields the rgba value associated with overlaying color2 on top
      of color1 followed by any additional colors (overlaid left to right). This respects alpha
      values when calculating the results.
    Note that colors may be lists of colors, in which case a matrix of RGBA values is yielded.
    '''
    args = list(args)
    args.insert(0, color1)
    rgba = np.asarray([0.5,0.5,0.5,0])
    for c in args:
        c = to_rgba(c)
        a = c[...,3]
        a0 = rgba[...,3]
        if   np.isclose(a0, 0).all(): rgba = np.ones(rgba.shape) * c
        elif np.isclose(a,  0).all(): continue
        else:                         rgba = times(a, c) + times(1-a, rgba)
    return rgba 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ones [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 ones [as 別名]
def jacobian(self, p, into=None):
        # transpose to be 3 x 2 x n
        p = np.transpose(np.reshape(p, (-1, 3, 2)), (1,2,0))
        # First, get the two legs...
        (dx_ab, dy_ab) = p[1] - p[0]
        (dx_ac, dy_ac) = p[2] - p[0]
        (dx_bc, dy_bc) = p[2] - p[1]
        # now, the area is half the z-value of the cross-product...
        sarea0 = 0.5 * (dx_ab*dy_ac - dx_ac*dy_ab)
        # but we want to abs it
        dsarea0 = np.sign(sarea0)
        z = np.transpose([[-dy_bc,dx_bc], [dy_ac,-dx_ac], [-dy_ab,dx_ab]], (2,0,1))
        z = times(0.5*dsarea0, z)
        m = numel(p)
        n = p.shape[2]
        ii = (np.arange(n) * np.ones([6, n])).T.flatten()
        z = sps.csr_matrix((z.flatten(), (ii, np.arange(len(ii)))), shape=(n, m))
        return safe_into(into, z) 

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