Python numpy.ones_like() 使用实例

The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.

Example 1

def load_ROI_mask(self):

        proxy = nib.load(self.FLAIR_FILE)
        image_array = np.asarray(proxy.dataobj)

        mask = np.ones_like(image_array)
        mask[np.where(image_array < 90)] = 0

        # img = nib.Nifti1Image(mask, proxy.affine)
        # nib.save(img, join(modalities_path,'mask.nii.gz'))

        struct_element_size = (20, 20, 20)
        mask_augmented = np.pad(mask, [(21, 21), (21, 21), (21, 21)], 'constant', constant_values=(0, 0))
        mask_augmented = binary_closing(mask_augmented, structure=np.ones(struct_element_size, dtype=bool)).astype(
            np.int)

        return mask_augmented[21:-21, 21:-21, 21:-21].astype('bool') 

Example 2

def blur(I, r):
    """
    This method performs like cv2.blur().

    Parameters
    ----------
    I: NDArray
        Filtering input
    r: int
        Radius of blur filter

    Returns
    -------
    q: NDArray
        Blurred output of I.
    """
    ones = np.ones_like(I, dtype=np.float32)
    N = box_filter(ones, r)
    ret = box_filter(I, r)
    return ret / N 

Example 3

def make_3d_mask(img_shape, center, radius, shape='sphere'):
    mask = np.zeros(img_shape)
    radius = np.rint(radius)
    center = np.rint(center)
    sz = np.arange(int(max(center[0] - radius, 0)), int(max(min(center[0] + radius + 1, img_shape[0]), 0)))
    sy = np.arange(int(max(center[1] - radius, 0)), int(max(min(center[1] + radius + 1, img_shape[1]), 0)))
    sx = np.arange(int(max(center[2] - radius, 0)), int(max(min(center[2] + radius + 1, img_shape[2]), 0)))
    sz, sy, sx = np.meshgrid(sz, sy, sx)
    if shape == 'cube':
        mask[sz, sy, sx] = 1.
    elif shape == 'sphere':
        distance2 = ((center[0] - sz) ** 2
                     + (center[1] - sy) ** 2
                     + (center[2] - sx) ** 2)
        distance_matrix = np.ones_like(mask) * np.inf
        distance_matrix[sz, sy, sx] = distance2
        mask[(distance_matrix <= radius ** 2)] = 1
    elif shape == 'gauss':
        z, y, x = np.ogrid[:mask.shape[0], :mask.shape[1], :mask.shape[2]]
        distance = ((z - center[0]) ** 2 + (y - center[1]) ** 2 + (x - center[2]) ** 2)
        mask = np.exp(- 1. * distance / (2 * radius ** 2))
        mask[(distance > 3 * radius ** 2)] = 0
    return mask 

Example 4

def sim_target_fixed(target_data, target_labels, sigma, idx, target_params):
    """
    Sets as target to have fixed similarity between all the training samples
    :param target_data: (not used)
    :param target_labels: (not used)
    :param sigma: not used
    :param idx: indices of the data samples to be used for the calculation of the similarity matrix
    :param target_params: expect to found the 'target_value' here
    :return: the similarity matrix and the corresponding mask
    """
    if 'target_value' not in target_params:
        target_params['target_value'] = 0.0

    Gt = np.ones((len(idx), len(idx)))
    Gt = Gt * target_params['target_value']
    Gt_mask = np.ones_like(Gt)

    return np.float32(Gt), np.float32(Gt_mask) 

Example 5

def getCylinderPoints(xc,zc,r):
    xLocOrig1 = np.arange(-r,r+r/10.,r/10.)
    xLocOrig2 = np.arange(r,-r-r/10.,-r/10.)
    # Top half of cylinder
    zLoc1 = np.sqrt(-xLocOrig1**2.+r**2.)+zc
    # Bottom half of cylinder
    zLoc2 = -np.sqrt(-xLocOrig2**2.+r**2.)+zc
    # Shift from x = 0 to xc
    xLoc1 = xLocOrig1 + xc*np.ones_like(xLocOrig1)
    xLoc2 = xLocOrig2 + xc*np.ones_like(xLocOrig2)

    topHalf = np.vstack([xLoc1,zLoc1]).T
    topHalf = topHalf[0:-1,:]
    bottomhalf = np.vstack([xLoc2,zLoc2]).T
    bottomhalf = bottomhalf[0:-1,:]

    cylinderPoints = np.vstack([topHalf,bottomhalf])
    cylinderPoints = np.vstack([cylinderPoints,topHalf[0,:]])
    return cylinderPoints 

Example 6

def test_basic(self):
        dts = [np.bool, np.int16, np.int32, np.int64, np.double, np.complex128,
               np.longdouble, np.clongdouble]
        for dt in dts:
            c = np.ones(53, dtype=np.bool)
            assert_equal(np.where( c, dt(0), dt(1)), dt(0))
            assert_equal(np.where(~c, dt(0), dt(1)), dt(1))
            assert_equal(np.where(True, dt(0), dt(1)), dt(0))
            assert_equal(np.where(False, dt(0), dt(1)), dt(1))
            d = np.ones_like(c).astype(dt)
            e = np.zeros_like(d)
            r = d.astype(dt)
            c[7] = False
            r[7] = e[7]
            assert_equal(np.where(c, e, e), e)
            assert_equal(np.where(c, d, e), r)
            assert_equal(np.where(c, d, e[0]), r)
            assert_equal(np.where(c, d[0], e), r)
            assert_equal(np.where(c[::2], d[::2], e[::2]), r[::2])
            assert_equal(np.where(c[1::2], d[1::2], e[1::2]), r[1::2])
            assert_equal(np.where(c[::3], d[::3], e[::3]), r[::3])
            assert_equal(np.where(c[1::3], d[1::3], e[1::3]), r[1::3])
            assert_equal(np.where(c[::-2], d[::-2], e[::-2]), r[::-2])
            assert_equal(np.where(c[::-3], d[::-3], e[::-3]), r[::-3])
            assert_equal(np.where(c[1::-3], d[1::-3], e[1::-3]), r[1::-3]) 

Example 7

def plot_series(series):
    plt.figure(1)
    # colors = [np.array([1, 0.1, 0.1]), np.array([0.1, 1, 0.1]), np.array([0.1, 0.1, 1])]
    colors = ['m', 'g', 'r', 'b', 'y']
    for i, s in enumerate(series):
        print(s['x'], s['y'], s['std'], s['label'])
        small_number = np.ones_like(s['x']) * (s['x'][1]*0.1)
        x_axis = np.where(s['x'] == 0, small_number, s['x'])
        plt.plot(x_axis, s['y'], color=colors[i], label=s['label'])
        plt.fill_between(x_axis, s['y'] - s['std'], s['y'] + s['std'], color=colors[i], alpha=0.2)
    plt.semilogx()
    plt.xlabel('MI reward bonus')
    plt.ylabel('Final intrinsic reward')
    plt.title('Final intrinsic reward in pointMDP with 10 good modes')
    plt.legend(loc='best')
    plt.show() 

Example 8

def predict(self, path):
        if 'env_infos' in path.keys() and 'full_path' in path['env_infos'].keys():
            expanded_path = tensor_utils.flatten_first_axis_tensor_dict(path['env_infos']['full_path'])
        else:  # when it comes from log_diagnostics it's already expanded (or if it was never aggregated)
            expanded_path = path

        bonus = self.visitation_bonus * self.predict_count(expanded_path) + \
                self.dist_from_reset_bonus * self.predict_dist_from_reset(expanded_path)
        if self.snn_H_bonus:  # I need the if because the snn bonus is only available when there are latents
            bonus += self.snn_H_bonus * self.predict_entropy(expanded_path)

        total_bonus = bonus + self.survival_bonus * np.ones_like(bonus)

        if 'env_infos' in path.keys() and 'full_path' in path['env_infos'].keys():
            aggregated_bonus = []
            full_path_rewards = path['env_infos']['full_path']['rewards']
            total_steps = 0
            for sub_rewards in full_path_rewards:
                aggregated_bonus.append(np.sum(total_bonus[total_steps:total_steps + len(sub_rewards)]))
                total_steps += len(sub_rewards)
            total_bonus = aggregated_bonus

        return np.array(total_bonus) 

Example 9

def _initialize_filter(self):

        """Set up spectral filter or dealiasing."""

        if self.use_filter:
            cphi=0.65*pi
            wvx=np.sqrt((self.k*self.dx)**2.+(self.l*self.dy)**2.)
            self.filtr = np.exp(-23.6*(wvx-cphi)**4.)
            self.filtr[wvx<=cphi] = 1.
            self.logger.info(' Using filter')
        elif self.dealias:
            self.filtr = np.ones_like(self.wv2)
            self.filtr[self.nx/3:2*self.nx/3,:] = 0.
            self.filtr[:,self.ny/3:2*self.ny/3] = 0.
            self.logger.info(' Dealiasing with 2/3 rule')
        else:
            self.filtr = np.ones_like(self.wv2)
            self.logger.info(' No dealiasing; no filter') 

Example 10

def _initialize_filter(self):

        """ Set up spectral filter or dealiasing."""

        if self.use_filter:
            cphi=0.65*pi
            wvx=np.sqrt((self.k*self.dx)**2.+(self.l*self.dy)**2.)
            self.filtr = np.exp(-23.6*(wvx-cphi)**4.)
            self.filtr[wvx<=cphi] = 1.
            self.logger.info(' Using filter')
        elif self.dealias:
            self.filtr = np.ones_like(self.wv2)
            self.filtr[self.nx//3:2*self.nx//3,:] = 0.
            self.filtr[:,self.ny//3:2*self.ny//3] = 0.
            self.logger.info(' Dealiasing with 2/3 rule')
        else:
            self.filtr = np.ones_like(self.wv2)
            self.logger.info(' No dealiasing; no filter') 

Example 11

def flux(self, time, planets = None, cadence = 'lc'):
    '''
    
    '''
    
    # Ensure it's a list
    if planets is None:
      planets = self.planets
    elif type(planets) is str:
      planets = [planets]
    
    # Compute flux for each planet
    flux = np.ones_like(time)
    for planet in planets:
      if cadence == 'lc':
        model = ps.Transit(per = self.period[planet], b = self.b[planet], RpRs = self.RpRs[planet], t0 = self.t0[planet], 
                           rhos = self.rhos, ecc = self.ecc[planet], w = self.w[planet] * np.pi / 180., u1 = self.u1, 
                           u2 = self.u2, times = self.times[planet])
      else:
        model = ps.Transit(per = self.period[planet], b = self.b[planet], RpRs = self.RpRs[planet], t0 = self.t0[planet], 
                           rhos = self.rhos, ecc = self.ecc[planet], w = self.w[planet] * np.pi / 180., u1 = self.u1, 
                           u2 = self.u2, times = self.times[planet], exptime = ps.KEPSHRTCAD)
      flux *= model(time)
    
    return flux 

Example 12

def test_flat_tensor_dot_tensor():
    """
    Ensure that a flattened argument axis is not unflattend in the result.

    """
    H = ng.make_axis(2)
    W = ng.make_axis(7)
    C = ng.make_axis(3)
    K = ng.make_axis(11)

    axes_a = ng.make_axes([H, W, C])
    a = ng.constant(np.ones(axes_a.lengths), axes=axes_a)
    flat_a = ng.flatten_at(a, 2)

    axes_b = ng.make_axes([C, K])
    b = ng.constant(np.ones(axes_b.lengths), axes=axes_b)

    result = ng.dot(b, flat_a)

    with ExecutorFactory() as factory:
        result_fun = factory.executor(result)
        result_val = result_fun()

    result_correct = np.ones_like(result_val) * C.length
    ng.testing.assert_allclose(result_val, result_correct) 

Example 13

def __call__(self, input_data, weights):
        '''
        input_data in this case is a numpy array with batch_size on axis 1
        and weights is a matrix with 1 column
        '''
        if self.state is None:
            self.state = np.ones_like(weights)

        if self.velocity is None:
            self.velocity = np.zeros_like(weights)

        gradient = - input_data.mean(axis=1)

        self.state[:] = self.decay_rate * self.state + \
            (1.0 - self.decay_rate) * np.square(gradient)

        self.velocity = self.velocity * self.momentum + \
            self.learning_rate * gradient / np.sqrt(self.state + self.epsilon) + \
            self.learning_rate * self.wdecay * weights
        weights[:] = weights - self.velocity

        return weights 

Example 14

def Affine_test(self,N,sizex,sizey,sizez,times,stop_time,typeofT,colors):
        for i in range(times):
            # Theta
            idx = np.random.uniform(-1, 1);idy = np.random.uniform(-1, 1);idz = np.random.uniform(-1, 1)
            swithx = np.random.uniform(0,1);swithy = np.random.uniform(0,1);swithz = np.random.uniform(0,1)
            rotatex = np.random.uniform(-1, 1);rotatey = np.random.uniform(-1, 1);rotatez = np.random.uniform(-1, 1)
            cx = np.array([idx,rotatey,rotatez,swithx]);cy = np.array([rotatex,idy,rotatez,swithy]);cz = np.array([rotatex,rotatey,idz,swithz])
            # Source Grid
            x = np.linspace(-sizex, sizex, N);y = np.linspace(-sizey, sizey, N);z = np.linspace(-sizez, sizez, N)
            x, y, z = np.meshgrid(x, y, z)
            xgs, ygs, zgs = x.flatten(), y.flatten(),z.flatten()
            gps = np.vstack([xgs, ygs, zgs, np.ones_like(xgs)]).T
            # transform
            xgt = np.dot(gps, cx);ygt = np.dot(gps, cy);zgt = np.dot(gps, cz)
            # display
            showIm = ShowImage()
            showIm.Show_transform(xgs,ygs,zgs,xgt,ygt,zgt,sizex,sizey,sizez,stop_time,typeofT,N,colors) 

Example 15

def pseudo_call_gene_low_level(simulationTime=None, rho=None, temperatureHat=None, densityHat=None, safetyFactor=None, 
                        ionMass=1, ionCharge=1, Lref=None, Bref=None, rhoStar=None, Tref=None, nref=None, checkpointSuffix=0):
    """Function  to emulate a call to GENE with the same input arguments and return values.
    
    Used for testing other code when the overhead of an actual startup of GENE is not needed.  Of course, this can only
    be used to test correctness of syntax, not correctness of values.
    """
    
    # check inputs have been provided
    for var in (simulationTime, rho, temperatureHat, densityHat, safetyFactor, Lref, Bref, rhoStar, Tref, nref):
        if var is None:
            #logging.error("Input variables must be provided in call_gene_low_level.")
            raise ValueError
            
    MPIrank = 1
    dVdxHat = np.ones_like(rho)
    sqrt_gxx = np.ones_like(rho)
    avgParticleFluxHat = np.ones_like(rho)
    avgHeatFluxHat = np.ones_like(rho)
    temperatureOutput = np.ones_like(rho)
    densityOutput = np.ones_like(rho)
    return (MPIrank, dVdxHat, sqrt_gxx, avgParticleFluxHat, avgHeatFluxHat, temperatureOutput, densityOutput) 

Example 16

def _add_noise(v, amplitude):
        """Add noise to an array v in the following way:.
        
                    noisy_v = (1+h) * v
           
        where h is a random noise with specified standard deviation.  The noise h is trimmed to 
        be zero close to both boundaries.
        
        Inputs:
          v              input to add noise to (array)           
          ampltitude     specified standard deviation of noise (scalar)
          tac            autocorrelation time measured in discrete samples (scalar)
        Outputs:
          noisy_v        v with noise
        """
        # generate noise that is constant throughout space
        h  = np.random.normal(scale=amplitude) * np.ones_like(v)  
        # damped the sides of the noise close to the boundaries
        h = dampen_sides(h)
        noisy_v = (1 + h) * v
        return noisy_v 

Example 17

def __call__(self, t, x, n):
        # Define the contributions to the H coefficients for the Shestakov Problem
        H1 = np.ones_like(x)
        #H7 = shestakov_nonlinear_diffusion.H7contrib_Source(x)
        H7 = source(x)
        (H2turb, H3, extradata) = self.turbhandler.Hcontrib_turbulent_flux(n)
        H4 = None
        H6 = None
        # add "other" diffusive contributions by specifying a diffusivity, H2 = V'D [but V' = 1 here]
        H2constdiff = 0.03
        
        def diffusivity_right(x):
            diffusivity = np.zeros_like(x)
            xr = 0.85
            D0 = 7
            diffusivity[x > xr] = D0
            return diffusivity
        
        H2 = H2turb + H2constdiff    
        #H2 = H2turb + H2constdiff + diffusivity_right(x)   # if adding const to right edge
        return (H1, H2, H3, H4, H6, H7, extradata) 

Example 18

def __call__(self, t, x, n):
        # Define the contributions to the H coefficients for the Shestakov Problem
        H1 = np.ones_like(x)
        #H7 = shestakov_nonlinear_diffusion.H7contrib_Source(x)
        H7 = source(x)
        (H2turb, H3, extradata) = self.turbhandler.Hcontrib_turbulent_flux(n)
        H4 = None
        H6 = None
        # add "other" diffusive contributions by specifying a diffusivity, H2 = V'D [but V' = 1 here]
        H2constdiff = 0.03
        
        def diffusivity_right(x):
            diffusivity = np.zeros_like(x)
            xr = 0.85
            D0 = 7
            diffusivity[x > xr] = D0
            return diffusivity
        
        H2 = H2turb + H2constdiff    
        #H2 = H2turb + H2constdiff + diffusivity_right(x)   # if adding const to right edge
        return (H1, H2, H3, H4, H6, H7, extradata) 

Example 19

def setup_parameters_different_grids_tango_inside():
    # set up radial grids with Tango's outer radial boundary radially inward that of GENE.
    simulationTime = 0.4
    Lref = 1.65
    Bref = 2.5
    majorRadius = 1.65
    minorRadius = 0.594
    rhoStar = 1/140
    checkpointSuffix = 999
    
    numRadialPtsTango = 100
    numRadialPtsGene = 80
    rhoTango = np.linspace(0.1, 0.8, numRadialPtsTango)      # rho = r/a
    rhoGene = np.linspace(0.2, 0.9, numRadialPtsGene)
    
    rTango = rhoTango * minorRadius    # physical radius r
    rGene = rhoGene * minorRadius
    safetyFactorGeneGrid = tango.parameters.analytic_safety_factor(rGene, minorRadius, majorRadius)
    
    e = 1.60217662e-19          # electron charge
    temperatureGeneGrid = 1000 * e * np.ones_like(rGene)
    densityTangoGrid = 1e19 * np.ones_like(rTango)
    densityGeneGrid = 1e19 * np.ones_like(rGene)
    gridMapper = tango.interfacegrids_gene.GridInterfaceTangoInside(rTango, rGene)
    return (simulationTime, rTango, rGene, temperatureGeneGrid, densityTangoGrid, densityGeneGrid, safetyFactorGeneGrid, Lref, Bref, majorRadius, minorRadius, rhoStar, gridMapper, checkpointSuffix) 

Example 20

def setup_parameters_different_grids_tango_outside():
    # set up radial grids with Tango's outer radial boundary radially inward that of GENE.
    simulationTime = 0.4
    Lref = 1.65
    Bref = 2.5
    majorRadius = 1.65
    minorRadius = 0.594
    rhoStar = 1/140
    checkpointSuffix = 999
    
    numRadialPtsTango = 100
    numRadialPtsGene = 80
    rhoTango = np.linspace(0.1, 0.9, numRadialPtsTango)      # rho = r/a
    rhoGene = np.linspace(0.2, 0.7, numRadialPtsGene)
    
    rTango = rhoTango * minorRadius    # physical radius r
    rGene = rhoGene * minorRadius
    safetyFactorGeneGrid = tango.parameters.analytic_safety_factor(rGene, minorRadius, majorRadius)
    
    e = 1.60217662e-19          # electron charge
    temperatureGeneGrid = 1000 * e * np.ones_like(rGene)
    densityTangoGrid = 1e19 * np.ones_like(rTango)
    densityGeneGrid = 1e19 * np.ones_like(rGene)
    gridMapper = tango.interfacegrids_gene.GridInterfaceTangoOutside(rTango, rGene)
    return (simulationTime, rTango, rGene, temperatureGeneGrid, densityTangoGrid, densityGeneGrid, safetyFactorGeneGrid, Lref, Bref, majorRadius, minorRadius, rhoStar, gridMapper, checkpointSuffix) 

Example 21

def test_energy_conservation_sech2disk_manyparticles():
    # Test that energy is conserved for a self-gravitating disk
    N= 101
    totmass= 1.
    sigma= 1.
    zh= 2.*sigma**2./totmass
    x= numpy.arctanh(2.*numpy.random.uniform(size=N)-1)*zh
    v= numpy.random.normal(size=N)*sigma
    v-= numpy.mean(v) # stabilize
    m= numpy.ones_like(x)/N*(1.+0.1*(2.*numpy.random.uniform(size=N)-1))
    g= wendy.nbody(x,v,m,0.05)
    E= wendy.energy(x,v,m)
    cnt= 0
    while cnt < 100:
        tx,tv= next(g)
        assert numpy.fabs(wendy.energy(tx,tv,m)-E) < 10.**-10., "Energy not conserved during simple N-body integration"
        cnt+= 1
    return None 

Example 22

def test_energy_conservation_sech2disk_manyparticles():
    # Test that energy is conserved for a self-gravitating disk
    N= 101
    totmass= 1.
    sigma= 1.
    zh= 2.*sigma**2./totmass
    x= numpy.arctanh(2.*numpy.random.uniform(size=N)-1)*zh
    v= numpy.random.normal(size=N)*sigma
    v-= numpy.mean(v) # stabilize
    m= numpy.ones_like(x)/N*(1.+0.1*(2.*numpy.random.uniform(size=N)-1))
    omega= 1.1
    g= wendy.nbody(x,v,m,0.05,omega=omega)
    E= wendy.energy(x,v,m,omega=omega)
    cnt= 0
    while cnt < 100:
        tx,tv= next(g)
        assert numpy.fabs(wendy.energy(tx,tv,m,omega=omega)-E) < 10.**-10., "Energy not conserved during simple N-body integration with external harmonic potential"
        cnt+= 1
    return None 

Example 23

def test_energy_conservation_sech2disk_manyparticles():
    # Test that energy is conserved for a self-gravitating disk
    N= 101
    totmass= 1.
    sigma= 1.
    zh= 2.*sigma**2./totmass
    x= numpy.arctanh(2.*numpy.random.uniform(size=N)-1)*zh
    v= numpy.random.normal(size=N)*sigma
    v-= numpy.mean(v) # stabilize
    m= numpy.ones_like(x)/N*(1.+0.1*(2.*numpy.random.uniform(size=N)-1))
    g= wendy.nbody(x,v,m,0.05,approx=True,nleap=1000)
    E= wendy.energy(x,v,m)
    cnt= 0
    while cnt < 100:
        tx,tv= next(g)
        assert numpy.fabs(wendy.energy(tx,tv,m)-E)/E < 10.**-6., "Energy not conserved during approximate N-body integration"
        cnt+= 1
    return None 

Example 24

def test_notracermasses():
    # approx should work with tracer sheets
    # Test that energy is conserved for a self-gravitating disk
    N= 101
    totmass= 1.
    sigma= 1.
    zh= 2.*sigma**2./totmass
    x= numpy.arctanh(2.*numpy.random.uniform(size=N)-1)*zh
    v= numpy.random.normal(size=N)*sigma
    v-= numpy.mean(v) # stabilize
    m= numpy.ones_like(x)/N*(1.+0.1*(2.*numpy.random.uniform(size=N)-1))
    m[N//2:]= 0.
    m*= 2.
    g= wendy.nbody(x,v,m,0.05,approx=True,nleap=1000)
    E= wendy.energy(x,v,m)
    cnt= 0
    while cnt < 100:
        tx,tv= next(g)
        assert numpy.fabs(wendy.energy(tx,tv,m)-E)/E < 10.**-6., "Energy not conserved during approximate N-body integration with some tracer particles"
        cnt+= 1
    return None 

Example 25

def test_energy_conservation_sech2disk_manyparticles():
    # Test that energy is conserved for a self-gravitating disk
    N= 101
    totmass= 1.
    sigma= 1.
    zh= 2.*sigma**2./totmass
    x= numpy.arctanh(2.*numpy.random.uniform(size=N)-1)*zh
    v= numpy.random.normal(size=N)*sigma
    v-= numpy.mean(v) # stabilize
    m= numpy.ones_like(x)/N*(1.+0.1*(2.*numpy.random.uniform(size=N)-1))
    omega= 1.1
    g= wendy.nbody(x,v,m,0.05,omega=omega,approx=True,nleap=1000)
    E= wendy.energy(x,v,m,omega=omega)
    cnt= 0
    while cnt < 100:
        tx,tv= next(g)
        assert numpy.fabs(wendy.energy(tx,tv,m,omega=omega)-E)/E < 10.**-6., "Energy not conserved during approximate N-body integration with external harmonic potential"
        cnt+= 1
    return None 

Example 26

def test_againstexact_sech2disk_manyparticles():
    # Test that the exact N-body and the approximate N-body agree
    N= 101
    totmass= 1.
    sigma= 1.
    zh= 2.*sigma**2./totmass
    x= numpy.arctanh(2.*numpy.random.uniform(size=N)-1)*zh
    v= numpy.random.normal(size=N)*sigma
    v-= numpy.mean(v) # stabilize
    m= numpy.ones_like(x)/N*(1.+0.1*(2.*numpy.random.uniform(size=N)-1))
    omega= 1.1
    g= wendy.nbody(x,v,m,0.05,approx=True,nleap=2000,omega=omega)
    ge= wendy.nbody(x,v,m,0.05,omega=omega)
    cnt= 0
    while cnt < 100:
        tx,tv= next(g)
        txe,tve= next(ge)
        assert numpy.all(numpy.fabs(tx-txe) < 10.**-5.), "Exact and approximate N-body give different positions"
        assert numpy.all(numpy.fabs(tv-tve) < 10.**-5.), "Exact and approximate N-body give different positions"
        cnt+= 1
    return None 

Example 27

def gain_substitution_scalar(gain, x, xwt):
    nants, nchan, nrec, _ = gain.shape
    newgain = numpy.ones_like(gain, dtype='complex')
    gwt = numpy.zeros_like(gain, dtype='float')
    
    # We are going to work with Jones 2x2 matrix formalism so everything has to be
    # converted to that format
    x = x.reshape(nants, nants, nchan, nrec, nrec)
    xwt = xwt.reshape(nants, nants, nchan, nrec, nrec)
    
    for ant1 in range(nants):
        for chan in range(nchan):
            # Loop over e.g. 'RR', 'LL, or 'xx', 'YY' ignoring cross terms
            top = numpy.sum(x[:, ant1, chan, 0, 0] *
                            gain[:, chan, 0, 0] * xwt[:, ant1, chan, 0, 0], axis=0)
            bot = numpy.sum((gain[:, chan, 0, 0] * numpy.conjugate(gain[:, chan, 0, 0]) *
                             xwt[:, ant1, chan, 0, 0]).real, axis=0)
            
            if bot > 0.0:
                newgain[ant1, chan, 0, 0] = top / bot
                gwt[ant1, chan, 0, 0] = bot
            else:
                newgain[ant1, chan, 0, 0] = 0.0
                gwt[ant1, chan, 0, 0] = 0.0
    return newgain, gwt 

Example 28

def test_balance_weights(self):
        labels = [1, 1, -1, -1, -1]
        predictions = [-1, -1, 1, 1, 1]  # all errors
        default_weights = np.abs(center(np.asarray(labels)))

        exp_error = balanced_classification_error(labels, predictions)
        error = balanced_classification_error(labels, predictions, default_weights)
        assert_equals(exp_error, error)

        null_weights = np.ones_like(labels)
        exp_error = classification_error(labels, predictions)
        error = balanced_classification_error(labels, predictions, null_weights)
        assert_equals(exp_error, error)

        # Balanced classes
        labels = [1, 1, 1, -1, -1, -1]
        predictions = [-1, -1, -1, 1, 1, 1]  # all errors
        exp_error = classification_error(labels, predictions)
        error = balanced_classification_error(labels, predictions)
        assert_equals(exp_error, error) 

Example 29

def test_balance_weights():
    """Test balanced classification error with custom weights."""
    labels = [1, 1, -1, -1, -1]
    predictions = [-1, -1, 1, 1, 1] # all errors
    default_weights = np.abs(center(np.asarray(labels)))

    exp_error = balanced_classification_error(labels, predictions)
    error = balanced_classification_error(labels, predictions, default_weights)
    assert_equals(exp_error, error)

    null_weights = np.ones_like(labels)
    exp_error = classification_error(labels, predictions)
    error = balanced_classification_error(labels, predictions, null_weights)
    assert_equals(exp_error, error)

    # Balanced classes
    labels = [1, 1, 1, -1, -1, -1]
    predictions = [-1, -1, -1, 1, 1, 1] # all errors
    exp_error = classification_error(labels, predictions)
    error = balanced_classification_error(labels, predictions)
    assert_equals(exp_error, error) 

Example 30

def initialize_labels(self, Y):

        y_nodes_flat = [y_val for y in Y for y_val in y.nodes]
        y_links_flat = [y_val for y in Y for y_val in y.links]
        self.prop_encoder_ = LabelEncoder().fit(y_nodes_flat)
        self.link_encoder_ = LabelEncoder().fit(y_links_flat)

        self.n_prop_states = len(self.prop_encoder_.classes_)
        self.n_link_states = len(self.link_encoder_.classes_)

        self.prop_cw_ = np.ones_like(self.prop_encoder_.classes_,
                                     dtype=np.double)
        self.link_cw_ = compute_class_weight(self.class_weight,
                                             self.link_encoder_.classes_,
                                             y_links_flat)

        self.link_cw_ /= self.link_cw_.min()

        logging.info('Setting node class weights {}'.format(", ".join(
            "{}: {}".format(lbl, cw) for lbl, cw in zip(
                self.prop_encoder_.classes_, self.prop_cw_))))

        logging.info('Setting link class weights {}'.format(", ".join(
            "{}: {}".format(lbl, cw) for lbl, cw in zip(
                self.link_encoder_.classes_, self.link_cw_)))) 

Example 31

def get_adagrad(learning_rate=0.5):
    """
    Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
    John Duchi, Elad Hazan and Yoram Singer, Journal of Machine Learning Research 12 (2011) 2121-2159
    http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf
    """
    sum_square_gradient = None

    def adagrad(gradient):
        nonlocal sum_square_gradient

        if sum_square_gradient is None:
            sum_square_gradient = np.ones_like(gradient)
        sum_square_gradient += gradient ** 2
        return learning_rate / np.sqrt(sum_square_gradient)

    return adagrad 

Example 32

def load_data(self):
        # Create the data using magic numbers to approximate the figure in
        # canevet_icml2016
        x = np.linspace(0, 1, self.N).astype(np.float32)
        ones = np.ones_like(x).astype(int)
        boundary = np.sin(4*(x + 0.5)**5)/3 + 0.5

        data = np.empty(shape=[self.N, self.N, 3], dtype=np.float32)
        data[:, :, 0] = 1-x
        for i in range(self.N):
            data[i, :, 1] = 1-x[i]
            data[i, :, 2] = 1 / (1 + np.exp(self.smooth*(x - boundary[i])))
            data[i, :, 2] = np.random.binomial(ones, data[i, :, 2])
        data = data.reshape(-1, 3)
        np.random.shuffle(data)

        # Create train and test arrays
        split = int(len(data)*self.test_split)
        X_train = data[:-split, :2]
        y_train = data[:-split, 2]
        X_test = data[-split:, :2]
        y_test = data[-split:, 2]

        return (X_train, y_train), (X_test, y_test) 

Example 33

def __init__(self, dataset, reweighting, model, large_batch=1024,
                 forward_batch_size=128, steps_per_epoch=300, recompute=2,
                 s_e=(1, 1), n_epochs=1):
        super(OnlineBatchSelectionSampler, self).__init__(
            dataset,
            reweighting,
            model,
            large_batch=large_batch,
            forward_batch_size=forward_batch_size
        )

        # The configuration of OnlineBatchSelection
        self.steps_per_epoch = steps_per_epoch
        self.recompute = recompute
        self.s_e = s_e
        self.n_epochs = n_epochs

        # Mutable variables to be updated
        self._batch = 0
        self._epoch = 0
        self._raw_scores = np.ones((len(dataset.train_data),))
        self._scores = np.ones_like(self._raw_scores)
        self._ranks = np.arange(len(dataset.train_data)) 

Example 34

def load_data(self):
        # Create the data using magic numbers to approximate the figure in
        # canevet_icml2016
        x = np.linspace(0, 1, self.N).astype(np.float32)
        ones = np.ones_like(x).astype(int)
        boundary = np.sin(4*(x + 0.5)**5)/3 + 0.5

        data = np.empty(shape=[self.N, self.N, 3], dtype=np.float32)
        data[:, :, 0] = 1-x
        for i in range(self.N):
            data[i, :, 1] = 1-x[i]
            data[i, :, 2] = 1 / (1 + np.exp(self.smooth*(x - boundary[i])))
            data[i, :, 2] = np.random.binomial(ones, data[i, :, 2])
        data = data.reshape(-1, 3)
        np.random.shuffle(data)

        # Create train and test arrays
        split = int(len(data)*self.test_split)
        X_train = data[:-split, :2]
        y_train = data[:-split, 2]
        X_test = data[-split:, :2]
        y_test = data[-split:, 2]

        return (X_train, y_train), (X_test, y_test) 

Example 35

def __init__(self, dataset, reweighting, model, large_batch=1024,
                 forward_batch_size=128, steps_per_epoch=300, recompute=2,
                 s_e=(1, 1), n_epochs=1):
        super(OnlineBatchSelectionSampler, self).__init__(
            dataset,
            reweighting,
            model,
            large_batch=large_batch,
            forward_batch_size=forward_batch_size
        )

        # The configuration of OnlineBatchSelection
        self.steps_per_epoch = steps_per_epoch
        self.recompute = recompute
        self.s_e = s_e
        self.n_epochs = n_epochs

        # Mutable variables to be updated
        self._batch = 0
        self._epoch = 0
        self._raw_scores = np.ones((len(dataset.train_data),))
        self._scores = np.ones_like(self._raw_scores)
        self._ranks = np.arange(len(dataset.train_data)) 

Example 36

def load_data(self):
        # Create the data using magic numbers to approximate the figure in
        # canevet_icml2016
        x = np.linspace(0, 1, self.N).astype(np.float32)
        ones = np.ones_like(x).astype(int)
        boundary = np.sin(4*(x + 0.5)**5)/3 + 0.5

        data = np.empty(shape=[self.N, self.N, 3], dtype=np.float32)
        data[:, :, 0] = 1-x
        for i in range(self.N):
            data[i, :, 1] = 1-x[i]
            data[i, :, 2] = 1 / (1 + np.exp(self.smooth*(x - boundary[i])))
            data[i, :, 2] = np.random.binomial(ones, data[i, :, 2])
        data = data.reshape(-1, 3)
        np.random.shuffle(data)

        # Create train and test arrays
        split = int(len(data)*self.test_split)
        X_train = data[:-split, :2]
        y_train = data[:-split, 2]
        X_test = data[-split:, :2]
        y_test = data[-split:, 2]

        return (X_train, y_train), (X_test, y_test) 

Example 37

def load_data(self):
        # Create the data using magic numbers to approximate the figure in
        # canevet_icml2016
        x = np.linspace(0, 1, self.N).astype(np.float32)
        ones = np.ones_like(x).astype(int)
        boundary = np.sin(4*(x + 0.5)**5)/3 + 0.5

        data = np.empty(shape=[self.N, self.N, 3], dtype=np.float32)
        data[:, :, 0] = 1-x
        for i in range(self.N):
            data[i, :, 1] = 1-x[i]
            data[i, :, 2] = 1 / (1 + np.exp(self.smooth*(x - boundary[i])))
            data[i, :, 2] = np.random.binomial(ones, data[i, :, 2])
        data = data.reshape(-1, 3)
        np.random.shuffle(data)

        # Create train and test arrays
        split = int(len(data)*self.test_split)
        X_train = data[:-split, :2]
        y_train = data[:-split, 2]
        X_test = data[-split:, :2]
        y_test = data[-split:, 2]

        return (X_train, y_train), (X_test, y_test) 

Example 38

def __init__(self, dataset, reweighting, model, large_batch=1024,
                 forward_batch_size=128, steps_per_epoch=300, recompute=2,
                 s_e=(1, 1), n_epochs=1):
        super(OnlineBatchSelectionSampler, self).__init__(
            dataset,
            reweighting,
            model,
            large_batch=large_batch,
            forward_batch_size=forward_batch_size
        )

        # The configuration of OnlineBatchSelection
        self.steps_per_epoch = steps_per_epoch
        self.recompute = recompute
        self.s_e = s_e
        self.n_epochs = n_epochs

        # Mutable variables to be updated
        self._batch = 0
        self._epoch = 0
        self._raw_scores = np.ones((len(dataset.train_data),))
        self._scores = np.ones_like(self._raw_scores)
        self._ranks = np.arange(len(dataset.train_data)) 

Example 39

def get_data(discrete_time):
    y_test, y_train, u_train = generate_weibull(A=real_a,
                                                B=real_b,
                                                # <np.inf -> impose censoring
                                                C=censoring_point,
                                                shape=[n_sequences,
                                                       n_timesteps, 1],
                                                discrete_time=discrete_time)
    # With random input it _should_ learn weight 0
    x_train = x_test = np.random.uniform(
        low=-1, high=1, size=[n_sequences, n_timesteps, n_features])

    # y_test is uncencored data
    y_test = np.append(y_test, np.ones_like(y_test), axis=-1)
    y_train = np.append(y_train, u_train, axis=-1)
    return y_train, x_train, y_test, x_test 

Example 40

def test_ignore_nans(self):
        """ Test that NaNs are ignored. """
        source = [np.ones((16,), dtype = np.float) for _ in range(10)]
        source.append(np.full_like(source[0], np.nan))
        product = cprod(source, ignore_nan = True)
        self.assertTrue(np.allclose(product, np.ones_like(product))) 

Example 41

def test_dtype(self):
        """ Test that dtype argument is working """
        source = [np.ones((16,), dtype = np.float) for _ in range(10)]
        product = cprod(source, dtype = np.int)
        self.assertTrue(np.allclose(product, np.ones_like(product)))
        self.assertEqual(product.dtype, np.int) 

Example 42

def test_trivial(self):
        """ Test a product of ones """
        source = [np.ones((16,), dtype = np.float) for _ in range(10)]
        product = last(iprod(source))
        self.assertTrue(np.allclose(product, np.ones_like(product))) 

Example 43

def test_ignore_nans(self):
        """ Test that NaNs are ignored. """
        source = [np.ones((16,), dtype = np.float) for _ in range(10)]
        source.append(np.full_like(source[0], np.nan))
        product = last(iprod(source, ignore_nan = True))
        self.assertTrue(np.allclose(product, np.ones_like(product))) 

Example 44

def test_dtype(self):
        """ Test that dtype argument is working """
        source = [np.ones((16,), dtype = np.float) for _ in range(10)]
        product = last(iprod(source, dtype = np.int))
        self.assertTrue(np.allclose(product, np.ones_like(product)))
        self.assertEqual(product.dtype, np.int) 

Example 45

def test_trivial(self):
        """ Test a product of ones """
        source = [np.ones((16,), dtype = np.float) for _ in range(10)]
        product = last(inanprod(source))
        self.assertTrue(np.allclose(product, np.ones_like(product))) 

Example 46

def composed_triangular_mesh(triangular_mesh_dict):
    start_time = time()
    print "--> Composing triangular mesh..."

    mesh = TriangularMesh()

    triangle_cell_matching = {}

    mesh_points = np.concatenate([triangular_mesh_dict[c].points.keys() for c in triangular_mesh_dict.keys()])
    mesh_point_positions = np.concatenate([triangular_mesh_dict[c].points.values() for c in triangular_mesh_dict.keys()])
    mesh.points = dict(zip(mesh_points,mesh_point_positions))

    mesh_triangles = np.concatenate([triangular_mesh_dict[c].triangles.values() for c in triangular_mesh_dict.keys()])
    mesh.triangles = dict(zip(np.arange(len(mesh_triangles)),mesh_triangles))

    mesh_cells = np.concatenate([c*np.ones_like(triangular_mesh_dict[c].triangles.keys()) for c in triangular_mesh_dict.keys()])
    triangle_cell_matching = dict(zip(np.arange(len(mesh_triangles)),mesh_cells))


    # for c in triangular_mesh_dict.keys():
    #     cell_start_time = time()

    #     cell_mesh = triangular_mesh_dict[c]
    #     # mesh_point_max_id = np.max(mesh.points.keys()) if len(mesh.points)>0 else 0
    #     mesh.points.update(cell_mesh.points)

    #     if len(cell_mesh.triangles)>0:
    #         mesh_triangle_max_id = np.max(mesh.triangles.keys()) if len(mesh.triangles)>0 else 0
    #         mesh.triangles.update(dict(zip(list(np.array(cell_mesh.triangles.keys())+mesh_triangle_max_id),cell_mesh.triangles.values())))
    #         triangle_cell_matching.update(dict(zip(list(np.array(cell_mesh.triangles.keys())+mesh_triangle_max_id),[c for f in cell_mesh.triangles]))) 

    #     cell_end_time = time()
    #     print "  --> Adding cell ",c," (",len(cell_mesh.triangles)," triangles )    [",cell_end_time-cell_start_time,"s]"

    end_time = time()
    print "<-- Composing triangular mesh     [",end_time-start_time,"]"
    return mesh, triangle_cell_matching 

Example 47

def _loess_predict(X, y_tr, X_pred, bandwidth):
    X_tr = np.column_stack((np.ones_like(X), X))
    X_te = np.column_stack((np.ones_like(X_pred), X_pred))
    y_te = []
    for x in X_te:
        ws = np.exp(-np.sum((X_tr - x)**2, axis=1) / (2 * bandwidth**2))
        W = scipy.sparse.dia_matrix((ws, 0), shape=(X_tr.shape[0],) * 2)
        theta = np.linalg.pinv(X_tr.T.dot(W.dot(X_tr))).dot(X_tr.T.dot(W.dot(y_tr)))
        y_te.append(np.dot(x, theta))
    return np.array(y_te) 

Example 48

def __init__(self, X, Y, batch_size, cropsize=0):
        
        assert len(X) == len(Y), 'X and Y must be the same length {}!={}'.format(len(X),len(Y))
        print('starting balanced generator')
        self.X = X
        self.Y = Y
        self.cropsize=cropsize
        self.batch_size = int(batch_size)
        self.pmatrix = np.ones_like(self.Y)
        self.reset() 

Example 49

def gamma_fullsum_grad(
    gamma,
    node_vec,
    eventmemes,
    etimes,
    T,
    mu,
    alpha,
    omega,
    W,
    beta,
    kernel_evaluate,
    K_evaluate,
    ):
    '''
    it actually returns negated gradient.
    '''

    gradres = np.ones_like(gamma) * -T * np.sum(mu)
    for (eventidx, (etime1, infected_u, eventmeme)) in \
        enumerate(izip(etimes, node_vec, eventmemes)):
        gradres[eventmeme] += mu[infected_u] \
            / np.exp(event_nonapproximated_logintensity(
            infected_u,
            eventmeme,
            etime1,
            T,
            etimes[:eventidx],
            node_vec[:eventidx],
            eventmemes[:eventidx],
            mu,
            gamma,
            omega,
            alpha,
            kernel_evaluate,
            ))
    return -gradres


# ===== 

Example 50

def relu(x, deriv=False):
        '''
        Rectifier function
        :param x: np.array
        :param deriv: derivate wanted ?
        :return:
        '''
        if deriv:
            return np.ones_like(x) * (x > 0)

        return x * (x > 0) 
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