Python numpy.matrix() Examples

def cost(params, Y, R, num_features):
    Y = np.matrix(Y)  # (1682, 943)
    R = np.matrix(R)  # (1682, 943)
    num_movies = Y.shape[0]
    num_users = Y.shape[1]
    
    # reshape the parameter array into parameter matrices
    X = np.matrix(np.reshape(params[:num_movies * num_features], (num_movies, num_features)))  # (1682, 10)
    Theta = np.matrix(np.reshape(params[num_movies * num_features:], (num_users, num_features)))  # (943, 10)
    
    # initializations
    J = 0
    
    # compute the cost
    error = np.multiply((X * Theta.T) - Y, R)  # (1682, 943)
    squared_error = np.power(error, 2)  # (1682, 943)
    J = (1. / 2) * np.sum(squared_error)
    
    return J 

def heston_construct_correlated_path(params: ModelParameters,
                                     brownian_motion_one: np.array):
    """
    This method is a simplified version of the Cholesky decomposition method
    for just two assets. It does not make use of matrix algebra and is therefore
    quite easy to implement.

    Arguments:
        params : ModelParameters
            The parameters for the stochastic model.
        brownian_motion_one : np.array
            (Not filled)

    Returns:
        A correlated brownian motion path.
    """
    # We do not multiply by sigma here, we do that in the Heston model
    sqrt_delta = np.sqrt(params.all_delta)
    # Construct a path correlated to the first path
    brownian_motion_two = []
    for i in range(params.all_time - 1):
        term_one = params.cir_rho * brownian_motion_one[i]
        term_two = np.sqrt(1 - pow(params.cir_rho, 2)) * random.normalvariate(0, sqrt_delta)
        brownian_motion_two.append(term_one + term_two)
    return np.array(brownian_motion_one), np.array(brownian_motion_two) 

def _icp_find_rigid_transform(p_from, p_target):
	######### flag = 1 for SVD 
	######### flag = 2 for Gaussian
	A, B = np.copy(p_from), np.copy(p_target)

	centroid_A = np.mean(A, axis=0)
	centroid_B = np.mean(B, axis=0)

	A -= centroid_A						# 500x3
	B -= centroid_B						# 500x3

	H = np.dot(A.T, B)					# 3x3
	# print(H)
	U, S, Vt = np.linalg.svd(H)
	# H_new = U*np.matrix(np.diag(S))*Vt
	# print(H_new)
	R = np.dot(Vt.T, U.T)

	# special reflection case
	if np.linalg.det(R) < 0:
		Vt[2,:] *= -1
		R = np.dot(Vt.T, U.T)
	t = np.dot(-R, centroid_A) + centroid_B
	return R, t 

def _get_equirectangular_raster(self, out_size):
        # Set image size (2x1 aspect ratio)
        rows = out_size
        cols = 2*out_size
        # Calculate longitude of each column.
        theta_x = np.linspace(-pi, pi, cols, endpoint=False, dtype='float32')
        cos_x = np.cos(theta_x).reshape(1,cols)
        sin_x = np.sin(theta_x).reshape(1,cols)
        # Calculate lattitude of each row.
        ystep = pi / rows
        theta_y = np.linspace(-pi/2 + ystep/2, pi/2 - ystep/2, rows, dtype='float32')
        cos_y = np.cos(theta_y).reshape(rows,1)
        sin_y = np.sin(theta_y).reshape(rows,1)
        # Calculate X, Y, and Z coordinates for each output pixel.
        x = cos_y * cos_x
        y = sin_y * np.ones((1,cols), dtype='float32')
        z = cos_y * sin_x
        # Vectorize the coordinates in raster order.
        xyz = np.matrix([x.ravel(), y.ravel(), z.ravel()])
        return [xyz, rows, cols]

    # Convert all lens parameters to a state vector. See also: optimize() 

def render_cubemap(self, out_size, mode='blend'):
        # Create coordinate arrays.
        cvec = np.arange(out_size, dtype='float32') - out_size/2        # Coordinate range [-S/2, S/2)
        vec0 = np.ones(out_size*out_size, dtype='float32') * out_size/2 # Constant vector +S/2
        vec1 = np.repeat(cvec, out_size)                                # Increment every N steps
        vec2 = np.tile(cvec, out_size)                                  # Sweep N times
        # Create XYZ coordinate vectors and render each cubemap face.
        render = lambda(xyz): self._render(xyz, out_size, out_size, mode)
        xm = render(np.matrix([-vec0, vec1, vec2]))     # -X face
        xp = render(np.matrix([vec0, vec1, -vec2]))     # +X face
        ym = render(np.matrix([-vec1, -vec0, vec2]))    # -Y face
        yp = render(np.matrix([vec1, vec0, vec2]))      # +Y face
        zm = render(np.matrix([-vec2, vec1, -vec0]))    # -Z face
        zp = render(np.matrix([vec2, vec1, vec0]))      # +Z face
        # Concatenate the individual faces in canonical order:
        # https://en.wikipedia.org/wiki/Cube_mapping#Memory_Addressing
        img_mat = np.concatenate([zp, zm, ym, yp, xm, xp], axis=0)
        return Image.fromarray(img_mat)

    # Get XYZ vectors for an equirectangular render, in raster order.
    # (Each row left to right, with rows concatenates from top to bottom.) 

def set_state(self, new_state):
        '''
        Set Hipster's new type.
        This comes from evaling the env.
        Args:
            new_state: the agent's new state
        Returns:
            None
        '''
        old_type = self.ntype
        self.state = new_state
        self.ntype = STATE_MAP[new_state]
        self.env.change_agent_type(self, old_type, self.ntype)
        # Unlike the forest fire model, we don't here update environment's cell's
        # transition matrices. This is because the cell's transition matrix depends
        # on all the agents near it, not just one. 

def add_pixels(self, uv_px, img1d, weight=None):
        # Lookup row & column for each in-bounds coordinate.
        mask = self.get_mask(uv_px)
        xx = uv_px[0,mask]
        yy = uv_px[1,mask]
        # Update matrix according to assigned weight.
        if weight is None:
            img1d[mask] = self.img[yy,xx]
        elif np.isscalar(weight):
            img1d[mask] += self.img[yy,xx] * weight
        else:
            w1 = np.asmatrix(weight, dtype='float32')
            w3 = w1.transpose() * np.ones((1,3))
            img1d[mask] += np.multiply(self.img[yy,xx], w3[mask])


# A panorama image made from several FisheyeImage sources.
# TODO: Add support for supersampled anti-aliasing filters. 

def get_pre(self, agent, n_census):

        trans_str = ""

        d, total = self.dir_info(agent)

        if type(agent) == Zombie:
            trans_str += self.zombie_trans(d, total)
        else:
            trans_str += self.human_trans(d, total)

        trans_matrix = markov.from_matrix(np.matrix(trans_str))
        return trans_matrix
       
    # Finds out which direction (NORTH, SOUTH, EAST, WEST) as more of the 
    # opposite agent type depending on what agent we are dealing with
    # CAN'T GET RID OF OLD METHOD OF MOVEMENT DUE TO ERRORS
    # IN OTHER SCRIPTS 

def cost0(params, input_size, hidden_size, num_labels, X, y, learning_rate):
    m = X.shape[0]
    X = np.matrix(X)
    y = np.matrix(y)
    
    # reshape the parameter array into parameter matrices for each layer
    theta1 = np.matrix(np.reshape(params[:hidden_size * (input_size + 1)], (hidden_size, (input_size + 1))))
    theta2 = np.matrix(np.reshape(params[hidden_size * (input_size + 1):], (num_labels, (hidden_size + 1))))
    
    # run the feed-forward pass
    a1, z2, a2, z3, h = forward_propagate(X, theta1, theta2)
    
    # compute the cost
    J = 0
    for i in range(m):
        first_term = np.multiply(-y[i,:], np.log(h[i,:]))
        second_term = np.multiply((1 - y[i,:]), np.log(1 - h[i,:]))
        J += np.sum(first_term - second_term)
    
    J = J / m
    
    return J 

def gradientReg(theta, X, y, learningRate):
    theta = np.matrix(theta)
    X = np.matrix(X)
    y = np.matrix(y)
    
    parameters = int(theta.ravel().shape[1])
    grad = np.zeros(parameters)
    
    error = sigmoid(X * theta.T) - y
    
    for i in range(parameters):
        term = np.multiply(error, X[:,i])
        
        if (i == 0):
            grad[i] = np.sum(term) / len(X)
        else:
            grad[i] = (np.sum(term) / len(X)) + ((learningRate / len(X)) * theta[:,i])
    
    return grad 

def controller_lqr_discrete_from_continuous_time(A, B, Q, R, dt):
    """Solve the discrete time LQR controller for a continuous time system.
    
    A and B are system matrices, describing the systems dynamics:
     dx/dt = A x + B u
    
    The controller minimizes the infinite horizon quadratic cost function:
     cost = integral (x.T*Q*x + u.T*R*u) dt
    where Q is a positive semidefinite matrix, and R is positive definite matrix.
    
    The controller is implemented to run at discrete times, at a rate given by
     onboard_dt, 
    i.e. u[k] = -K*x(k*t)
    Discretization is done by zero order hold.
    
    Returns K, X, eigVals:
    Returns gain the optimal gain K, the solution matrix X, and the closed loop system eigenvalues.
    The optimal input is then computed as:
     input: u = -K*x
    """
    #ref Bertsekas, p.151
    
    Ad, Bd = analysis.discretise_time(A, B, dt)

    return controller_lqr_discrete_time(Ad, Bd, Q, R) 

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 

def __init__(self, src_file, lens=None):
        # Load the image file, and convert to a numpy matrix.
        self._update_img(Image.open(src_file))
        # Set lens parameters.
        if lens is None:
            self.lens = FisheyeLens(self.rows, self.cols)
        else:
            self.lens = lens

    # Update image matrix and corresponding size variables. 

def test_system_norm_Hinf(self):
        for matType in [np.matrix, np.array]:
            A = matType([[-1,2],[0,-3]])
            B = matType([[0,1]]).T
            C = matType([[1,0]])
            
            hinfnorm = controlpy.analysis.system_norm_Hinf(A, B, C)
            
            hinfnorm_lmi = sys_norm_hinf_LMI(A, B, C)

            tol = 1e-3
            self.assertLess(np.linalg.norm(hinfnorm-hinfnorm_lmi), tol)
           
    #TODO:
    # - observability tests, similar to controllability 

def controller_lqr(A, B, Q, R):
    """Solve the continuous time LQR controller for a continuous time system.
    
    A and B are system matrices, describing the systems dynamics:
     dx/dt = A x + B u
    
    The controller minimizes the infinite horizon quadratic cost function:
     cost = integral (x.T*Q*x + u.T*R*u) dt
    
    where Q is a positive semidefinite matrix, and R is positive definite matrix.
    
    Returns K, X, eigVals:
    Returns gain the optimal gain K, the solution matrix X, and the closed loop system eigenvalues.
    The optimal input is then computed as:
     input: u = -K*x
    """
    #ref Bertsekas, p.151

    #first, try to solve the ricatti equation
    X = scipy.linalg.solve_continuous_are(A, B, Q, R)
    
    #compute the LQR gain
    K = np.dot(np.linalg.inv(R),(np.dot(B.T,X)))
    
    eigVals = np.linalg.eigvals(A-np.dot(B,K))
    
    return K, X, eigVals 

def controller_lqr_discrete_time(A, B, Q, R):
    """Solve the discrete time LQR controller for a discrete time system.
    
    A and B are system matrices, describing the systems dynamics:
     x[k+1] = A x[k] + B u[k]
    
    The controller minimizes the infinite horizon quadratic cost function:
     cost = sum x[k].T*Q*x[k] + u[k].T*R*u[k]
    
    where Q is a positive semidefinite matrix, and R is positive definite matrix.
    
    Returns K, X, eigVals:
    Returns gain the optimal gain K, the solution matrix X, and the closed loop system eigenvalues.
    The optimal input is then computed as:
     input: u = -K*x
    """

    #first, try to solve the ricatti equation
    X = scipy.linalg.solve_discrete_are(A, B, Q, R)
    
    #compute the LQR gain
    K = np.dot(np.linalg.inv(np.dot(np.dot(B.T,X),B)+R),(np.dot(np.dot(B.T,X),A)))  
    
    eigVals = np.linalg.eigvals(A-np.dot(B,K))
    
    return K, X, eigVals 

def test_scalar_hurwitz(self):
        self.assertTrue(controlpy.analysis.is_hurwitz(np.matrix([[-1]])))
        self.assertTrue(controlpy.analysis.is_hurwitz(np.array([[-1]])))
        self.assertFalse(controlpy.analysis.is_hurwitz(np.matrix([[1]])))
        self.assertFalse(controlpy.analysis.is_hurwitz(np.array([[1]]))) 

def _render(self, xyz, rows, cols, mode):
        # Allocate Nx3 or Nx1 "1D" pixel-list (raster-order).
        img1d = np.zeros((rows*cols, self.clrs), dtype='float32')
        # Determine rendering mode:
        if mode == 'overwrite':
            # Simplest mode: Draw first, then blindly overwrite second.
            for src in self.sources:
                uv = src.get_uv(xyz)
                src.add_pixels(uv, img1d)
        elif mode == 'align':
            # Alignment mode: Draw each one at 50% intensity.
            for src in self.sources:
                uv = src.get_uv(xyz)
                src.add_pixels(uv, img1d, 0.5)
        elif mode == 'blend':
            # Linear nearest-source blending.
            uv_list = []
            wt_list = []
            wt_total = np.zeros(rows*cols, dtype='float32')
            # Calculate per-image and total weight matrices.
            for src in self.sources:
                uv = src.get_uv(xyz)
                wt = src.get_weight(uv)
                uv_list.append(uv)
                wt_list.append(wt)
                wt_total += wt
            # Render overall image using calculated weights.
            for n in range(len(self.sources)):
                wt_norm = wt_list[n] / wt_total
                self.sources[n].add_pixels(uv_list[n], img1d, wt_norm)
        else:
            raise ValueError('Invalid render mode.')
        # Convert to fixed-point image matrix and return.
        img2d = np.reshape(img1d, (rows, cols, self.clrs))
        return np.asarray(img2d, dtype=self.dtype)

    # Compute a normalized alignment score, based on size of overlap and
    # the pixel-differences in that region.  Note: Lower = Better. 

def _remove_useless_states(self):
        """Check for states that don't do anything, and remove them.

        Scan the A, B, and C matrices for rows or columns of zeros.  If the
        zeros are such that a particular state has no effect on the input-output
        dynamics, then remove that state from the A, B, and C matrices.

        """

        # Search for useless states and get indices of these states.
        #
        # Note: shape from np.where depends on whether we are storing state
        # space objects as np.matrix or np.array.  Code below will work
        # correctly in either case.
        ax1_A = np.where(~self.A.any(axis=1))[0]
        ax1_B = np.where(~self.B.any(axis=1))[0]
        ax0_A = np.where(~self.A.any(axis=0))[-1]
        ax0_C = np.where(~self.C.any(axis=0))[-1]
        useless_1 = np.intersect1d(ax1_A, ax1_B, assume_unique=True)
        useless_2 = np.intersect1d(ax0_A, ax0_C, assume_unique=True)
        useless = np.union1d(useless_1, useless_2)

        # Remove the useless states.
        self.A = delete(self.A, useless, 0)
        self.A = delete(self.A, useless, 1)
        self.B = delete(self.B, useless, 0)
        self.C = delete(self.C, useless, 1)

        self.states = self.A.shape[0]
        self.inputs = self.B.shape[1]
        self.outputs = self.C.shape[0] 

def __init__(self, rows=1024, cols=1024):
        # Fisheye lens parameters.
        self.fov_deg = 180
        self.radius_px = min(rows,cols) / 2
        # Pixel coordinates of the optical axis (X,Y).
        self.center_px = np.matrix([[cols/2], [rows/2]])
        # Quaternion mapping intended to actual optical axis.
        self.center_qq = [1, 0, 0, 0] 

def test_system_norm_H2(self):
        for matType in [np.matrix, np.array]:
            A = matType([[-1,2],[0,-3]])
            B = matType([[0,1]]).T
            C = matType([[1,0]])
            
            h2norm = controlpy.analysis.system_norm_H2(A, B, C)
            
            h2norm_lmi = sys_norm_h2_LMI(A, B, C)

            tol = 1e-3
            self.assertLess(np.linalg.norm(h2norm-h2norm_lmi), tol) 

def test_time_discretisation(self):
        for matType in [np.matrix, np.array]:
            Ac = matType([[0,1],[0,0]])
            Bc = matType([[0],[1]])
             
            dt = 0.1
             
            Ad = matType([[1,dt],[0,1]])
            Bd = matType([[dt**2/2],[dt]])
            for i in range(1000):
                T = matType(np.random.normal(size=[2,2]))
                Tinv = np.linalg.inv(T)
 
                AAc = np.dot(np.dot(T,Ac),Tinv)
                BBc = np.dot(T,Bc)
 
                AAd = np.dot(np.dot(T,Ad),Tinv)
                BBd = np.dot(T,Bd)
                 
                AAd2, BBd2 = controlpy.analysis.discretise_time(AAc, BBc, dt)
                 
                self.assertLess(np.linalg.norm(AAd-AAd2), 1e-6)
                self.assertLess(np.linalg.norm(BBd-BBd2), 1e-6)
                 
                 
            #test some random systems against Euler discretisation
            nx = 20
            nu = 20
            dt = 1e-6
            tol = 1e-3
            for i in range(1000):
                Ac = matType(np.random.normal(size=[nx,nx]))
                Bc = matType(np.random.normal(size=[nx,nu]))
                 
                Ad1 = np.identity(nx)+Ac*dt
                Bd1 = Bc*dt
                 
                Ad2, Bd2 = controlpy.analysis.discretise_time(Ac, Bc, dt)
                 
                self.assertLess(np.linalg.norm(Ad1-Ad2), tol)
                self.assertLess(np.linalg.norm(Bd1-Bd2), tol) 

def testReturnsCorrectLossWithClassIndices(self):
    prediction_tensor = tf.constant([[[-100, 100, -100, 100],
                                      [100, -100, -100, -100],
                                      [100, 0, -100, 100],
                                      [-100, -100, 100, -100]],
                                     [[-100, 0, 100, 100],
                                      [-100, 100, -100, 100],
                                      [100, 100, 100, 100],
                                      [0, 0, -1, 100]]], tf.float32)
    target_tensor = tf.constant([[[0, 1, 0, 0],
                                  [1, 0, 0, 1],
                                  [1, 0, 0, 0],
                                  [0, 0, 1, 1]],
                                 [[0, 0, 1, 0],
                                  [0, 1, 0, 0],
                                  [1, 1, 1, 0],
                                  [1, 0, 0, 0]]], tf.float32)
    weights = tf.constant([[1, 1, 1, 1],
                           [1, 1, 1, 0]], tf.float32)
    # Ignores the last class.
    class_indices = tf.constant([0, 1, 2], tf.int32)
    loss_op = losses.WeightedSigmoidClassificationLoss(True)
    loss = loss_op(prediction_tensor, target_tensor, weights=weights,
                   class_indices=class_indices)

    exp_loss = np.matrix([[0, 0, -math.log(.5), 0],
                          [-math.log(.5), 0, 0, 0]])
    with self.test_session() as sess:
      loss_output = sess.run(loss)
      self.assertAllClose(loss_output, exp_loss) 

def _imageToMatrix(self, image):
        """
        根据名称读取图片对象转化矩阵
        :param strName:
        :return: 返回矩阵
        """
        imgMat = np.matrix(image)
        return imgMat 

def icp_test(S, M, tree_M, M_sampled, tree_M_sampled, S_given, gt_pose, MAX_LOOPS, ftol):
	from math import sin, cos
	import matplotlib.pyplot as plt
	from mpl_toolkits.mplot3d import Axes3D
 
	icp = ICP(M_sampled, S, tree_M_sampled)
	start = time.time()

	matrix, points, itr, neighbor_idx = icp.compute(MAX_LOOPS, ftol)		# Perform the iterations.

	end = time.time()

	final_pose = helper.find_final_pose((np.linalg.inv(matrix)).reshape((1,4,4)))

	title = "Actual T (Red->Green): "
	for i in range(len(gt_pose[0])):
		if i>2:
			title += str(round(gt_pose[0][i]*(180/np.pi),2))
		else:
			title += str(gt_pose[0][i])
		title += ', '
	title += "\nPredicted T (Red->Blue): "

	for i in range(len(final_pose[0])):
		if i>2:
			title += str(round(final_pose[0,i]*(180/np.pi),3))
		else:
			title += str(round(final_pose[0,i],3))
		title += ', '

	title += '\nElapsed Time: '+str(np.round((end-start)*1000,3))+' ms'+' & Iterations: '+str(itr)
	title += ' & Method: ICP'

	rot = matrix[0:3,0:3]
	tra = np.reshape(np.transpose(matrix[0:3,3]), (3,1))
	transformed = np.matmul(rot, np.transpose(S_given)) + tra
	return final_pose, M, S_given, transformed.T, title, end-start, itr 

def check_convergence(M_prev, M, ftol):
	identityT = np.eye(4)
	errorT = np.dot(M, np.linalg.inv(M_prev))
	errorT = errorT - identityT
	errorT = errorT*errorT
	error = np.sum(errorT)

	converged = False
	if error < ftol:
		converged = True
	return converged

# def find_errors(final_rot, final_trans):
# 	import transforms3d
# 	import transforms3d.euler as t3d
# 	# Simple euler distand between translation part.
# 	gt_pose = [0.5, 0.2, 0.4, 40*(np.pi/180), 20*(np.pi/180), 30*(np.pi/180)]
# 	gt_position = gt_pose[0:3]				
# 	predicted_position = final_trans
# 	translation_error = np.sqrt(np.sum(np.square(gt_position - predicted_position)))
	

# 	# Convert euler angles rotation matrix.
# 	gt_euler = gt_pose[3:6]
# 	gt_mat = t3d.euler2mat(gt_euler[2],gt_euler[1],gt_euler[0],'szyx')

# 	# Multiply inverse of one rotation matrix with another rotation matrix.

# 	error_mat = np.dot(final_rot,np.linalg.inv(gt_mat))
# 	_,angle = transforms3d.axangles.mat2axangle(error_mat)			# Convert matrix to axis angle representation and that angle is error.
	
# 	return translation_error, abs(angle*(180/np.pi)) 

def _icp_Rt_to_matrix(R, t):
	# matrix M = [R, t; 0, 1]
	Rt = np.concatenate((R, np.expand_dims(t.T, axis=-1)), axis=1)
	a = np.concatenate((np.zeros_like(t), np.ones(1)))
	M = np.concatenate((Rt, np.expand_dims(a, axis=0)), axis=0)
	return M 

def testReturnsCorrectAnchorWiseLoss(self):
    prediction_tensor = tf.constant([[[-100, 100, -100],
                                      [100, -100, -100],
                                      [100, 0, -100],
                                      [-100, -100, 100]],
                                     [[-100, 0, 100],
                                      [-100, 100, -100],
                                      [100, 100, 100],
                                      [0, 0, -1]]], tf.float32)
    target_tensor = tf.constant([[[0, 1, 0],
                                  [1, 0, 0],
                                  [1, 0, 0],
                                  [0, 0, 1]],
                                 [[0, 0, 1],
                                  [0, 1, 0],
                                  [1, 1, 1],
                                  [1, 0, 0]]], tf.float32)
    weights = tf.constant([[1, 1, 1, 1],
                           [1, 1, 1, 0]], tf.float32)
    alpha = tf.constant(.5, tf.float32)
    loss_op = losses.BootstrappedSigmoidClassificationLoss(
        alpha, bootstrap_type='hard', anchorwise_output=True)
    loss = loss_op(prediction_tensor, target_tensor, weights=weights)

    exp_loss = np.matrix([[0, 0, -math.log(.5), 0],
                          [-math.log(.5), 0, 0, 0]])
    with self.test_session() as sess:
      loss_output = sess.run(loss)
      self.assertAllClose(loss_output, exp_loss) 

def testReturnsCorrectAnchorWiseLoss(self):
    prediction_tensor = tf.constant([[[-100, 100, -100],
                                      [100, -100, -100],
                                      [0, 0, -100],
                                      [-100, -100, 100]],
                                     [[-100, 0, 0],
                                      [-100, 100, -100],
                                      [-100, 100, -100],
                                      [100, -100, -100]]], tf.float32)
    target_tensor = tf.constant([[[0, 1, 0],
                                  [1, 0, 0],
                                  [1, 0, 0],
                                  [0, 0, 1]],
                                 [[0, 0, 1],
                                  [0, 1, 0],
                                  [0, 1, 0],
                                  [1, 0, 0]]], tf.float32)
    weights = tf.constant([[1, 1, .5, 1],
                           [1, 1, 1, 0]], tf.float32)
    loss_op = losses.WeightedSoftmaxClassificationLoss(True)
    loss = loss_op(prediction_tensor, target_tensor, weights=weights)

    exp_loss = np.matrix([[0, 0, - 0.5 * math.log(.5), 0],
                          [-math.log(.5), 0, 0, 0]])
    with self.test_session() as sess:
      loss_output = sess.run(loss)
      self.assertAllClose(loss_output, exp_loss) 

def testReturnsCorrectAnchorWiseLoss(self):
    prediction_tensor = tf.constant([[[-100, 100, -100],
                                      [100, -100, -100],
                                      [100, 0, -100],
                                      [-100, -100, 100]],
                                     [[-100, 0, 100],
                                      [-100, 100, -100],
                                      [100, 100, 100],
                                      [0, 0, -1]]], tf.float32)
    target_tensor = tf.constant([[[0, 1, 0],
                                  [1, 0, 0],
                                  [1, 0, 0],
                                  [0, 0, 1]],
                                 [[0, 0, 1],
                                  [0, 1, 0],
                                  [1, 1, 1],
                                  [1, 0, 0]]], tf.float32)
    weights = tf.constant([[1, 1, 1, 1],
                           [1, 1, 1, 0]], tf.float32)
    loss_op = losses.WeightedSigmoidClassificationLoss(True)
    loss = loss_op(prediction_tensor, target_tensor, weights=weights)

    exp_loss = np.matrix([[0, 0, -math.log(.5), 0],
                          [-math.log(.5), 0, 0, 0]])
    with self.test_session() as sess:
      loss_output = sess.run(loss)
      self.assertAllClose(loss_output, exp_loss)