Python numpy.Inf方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def sys_norm_h2_LMI(Acl, Bdisturbance, C):
    #doesn't work very well, if problem poorly scaled Riccati works better.
    #Dullerud p 210
    n = Acl.shape[0]
    X = cvxpy.Semidef(n)
    Y = cvxpy.Semidef(n)

    constraints = [ Acl*X + X*Acl.T + Bdisturbance*Bdisturbance.T == -Y,
                  ]

    obj = cvxpy.Minimize(cvxpy.trace(Y))

    prob = cvxpy.Problem(obj, constraints)
    
    prob.solve()
    eps = 1e-16
    if np.max(np.linalg.eigvals((-Acl*X - X*Acl.T - Bdisturbance*Bdisturbance.T).value)) > -eps:
        print('Acl*X + X*Acl.T +Bdisturbance*Bdisturbance.T is not neg def.')
        return np.Inf

    if np.min(np.linalg.eigvals(X.value)) < eps:
        print('X is not pos def.')
        return np.Inf

    return np.sqrt(np.trace(C*X.value*C.T)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def generate_final_dataset(self):
        if self.sign == False:
            shift_var = 1
            self.bucket = True
        else:
            shift_var = -1
            self.bucket = False

        self.woe_summary[self.column + "_shift"] = self.woe_summary[self.column].shift(shift_var)

        if self.sign == False:
            self.woe_summary.loc[0, self.column + "_shift"] = -np.inf
            self.bins = np.sort(list(self.woe_summary[self.column]) + [np.Inf,-np.Inf])
        else:
            self.woe_summary.loc[len(self.woe_summary) - 1, self.column + "_shift"] = np.inf
            self.bins = np.sort(list(self.woe_summary[self.column]) + [np.Inf,-np.Inf])

        self.woe_summary["labels"] = self.woe_summary.apply(self.generate_bin_labels, axis=1)

        self.dataset["bins"] = pd.cut(self.dataset[self.column], self.bins, right=self.bucket, precision=0)

        self.dataset["bins"] = self.dataset["bins"].astype(str)
        self.dataset['bins'] = self.dataset['bins'].map(lambda x: x.lstrip('[').rstrip(')')) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def __init__(self, datasets="tmp", patience=7, fname=None, clean=False, verbose=False):
        """ 
        Args:
            patience (int): How long to wait after last time validation loss improved.
                            Default: 7
            verbose (bool): If True, prints a message for each validation loss improvement. 
                            Default: False
        """

        self.patience = patience
        self.verbose = verbose
        self.counter = 0
        self.best_score = None
        self.early_stop = False
        self.val_loss_min = np.Inf
        timstr = datetime.datetime.now().strftime("%m%d-%H%M%S")
        if fname is None:
            fname = datasets + "-" + timstr + "-" + self._random_str() + ".pt"
        self.fname = os.path.join(folder, fname)
        self.clean = clean 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def _reset(self):
    """Resets wait counter and cooldown counter.
    """
    if self.mode not in ['auto', 'min', 'max']:
      warnings.warn('Learning Rate Plateau Reducing mode %s is unknown, '
                    'fallback to auto mode.' % (self.mode), RuntimeWarning)
      self.mode = 'auto'
    if (self.mode == 'min' or
        (self.mode == 'auto' and 'acc' not in self.monitor)):
      self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
      self.best = np.Inf
    else:
      self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
      self.best = -np.Inf
    self.cooldown_counter = 0
    self.wait = 0
    self.lr_epsilon = self.min_lr * 1e-4 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def __init__(self, parent_sorting, *, unit_ids=None, renamed_unit_ids=None, start_frame=None, end_frame=None):
        SortingExtractor.__init__(self)
        start_frame, end_frame = self._cast_start_end_frame(start_frame, end_frame)
        self._parent_sorting = parent_sorting
        self._unit_ids = unit_ids
        self._renamed_unit_ids = renamed_unit_ids
        self._start_frame = start_frame
        self._end_frame = end_frame
        if self._unit_ids is None:
            self._unit_ids = self._parent_sorting.get_unit_ids()
        if self._renamed_unit_ids is None:
            self._renamed_unit_ids = self._unit_ids
        if self._start_frame is None:
            self._start_frame = 0
        if self._end_frame is None:
            self._end_frame = np.Inf
        self._original_unit_id_lookup = {}
        for i in range(len(self._unit_ids)):
            self._original_unit_id_lookup[self._renamed_unit_ids[i]] = self._unit_ids[i]
        self.copy_unit_properties(parent_sorting, unit_ids=self._renamed_unit_ids)
        self.copy_unit_spike_features(parent_sorting, unit_ids=self._renamed_unit_ids, start_frame=start_frame,
                                      end_frame=end_frame)
        self._kwargs = {'parent_sorting': parent_sorting.make_serialized_dict(), 'unit_ids': unit_ids,
                        'renamed_unit_ids': renamed_unit_ids, 'start_frame': start_frame, 'end_frame': end_frame} 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def get_unit_spike_train(self, unit_id, start_frame=None, end_frame=None):
        start_frame, end_frame = self._cast_start_end_frame(start_frame, end_frame)
        if start_frame is None:
            start_frame = 0
        if end_frame is None:
            end_frame = np.Inf
        original_unit_id = self._original_unit_id_lookup[unit_id]
        sf = self._start_frame + start_frame
        ef = self._start_frame + end_frame
        if sf < self._start_frame:
            sf = self._start_frame
        if ef > self._end_frame:
            ef = self._end_frame
        if ef == np.Inf:
            ef = None
        return self._parent_sorting.get_unit_spike_train(unit_id=original_unit_id, start_frame=sf,
                                                         end_frame=ef) - self._start_frame 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def __init__(self, params):
        self.params = params

        if self.params.hiddenRatio is not None:
            self.params.n_hidden = int(numpy.ceil(self.params.n_visible*self.params.hiddenRatio))

        # for 0-1 normlaization
        self.norm_max = numpy.ones((self.params.n_visible,)) * -numpy.Inf
        self.norm_min = numpy.ones((self.params.n_visible,)) * numpy.Inf
        self.n = 0

        self.rng = numpy.random.RandomState(1234)

        a = 1. / self.params.n_visible
        self.W = numpy.array(self.rng.uniform(  # initialize W uniformly
            low=-a,
            high=a,
            size=(self.params.n_visible, self.params.n_hidden)))

        self.hbias = numpy.zeros(self.params.n_hidden)  # initialize h bias 0
        self.vbias = numpy.zeros(self.params.n_visible)  # initialize v bias 0
        self.W_prime = self.W.T 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def on_train_begin(self):
        self.wait = 0       # Allow instances to be re-used
        self.best = np.Inf if self.monitor_op == np.less else -np.Inf 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def calculate_dV(self, TL, old_sInd, sInds, sd, slewTimes, tmpCurrentTimeAbs): 
        """Finds the change in velocity needed to transfer to a new star line of sight
        
        This method sums the total delta-V needed to transfer from one star
        line of sight to another. It determines the change in velocity to move from
        one station-keeping orbit to a transfer orbit at the current time, then from
        the transfer orbit to the next station-keeping orbit at currentTime + dt.
        Station-keeping orbits are modeled as discrete boundary value problems.
        This method can handle multiple indeces for the next target stars and calculates
        the dVs of each trajectory from the same starting star.
        
        Args:
            dt (float 1x1 ndarray):
                Number of days corresponding to starshade slew time
            TL (float 1x3 ndarray):
                TargetList class object
            nA (integer):
                Integer index of the current star of interest
            N  (integer):
                Integer index of the next star(s) of interest
            tA (astropy Time array):
                Current absolute mission time in MJD
                
        Returns:
            float nx6 ndarray:
                State vectors in rotating frame in normalized units
        """
        
        if old_sInd is None:
            dV = np.zeros(slewTimes.shape)
        else:
            dV = np.zeros(slewTimes.shape)
            badSlews_i,badSlew_j = np.where(slewTimes.value <  self.occ_dtmin.value)
            for i in range(len(sInds)):
                for t in range(len(slewTimes.T)):
                    dV[i,t] = self.dV_interp(slewTimes[i,t],sd[i].to('deg')) 
            dV[badSlews_i,badSlew_j] = np.Inf
        
        return dV*u.m/u.s 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def __init__(self,shape,var_axes=[(0,),(0,)], name=None, map_est=False):
        self.map_est = map_est
        
        # Initial variances
        self.zvar0_init= np.Inf
        self.zvar1_init= np.Inf
        
        nvars = 2
        dtype = np.float64
        BaseEst.__init__(self,shape=[shape,shape], var_axes=var_axes, dtype=dtype, name=name,\
            type_name='ReLUEst', nvars=nvars, cost_avail=True) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def __init__(self,y,shape,var_axes=(0,),thresh=0,perr=1e-6,\
                 name=None,var_init=np.Inf,dtype=np.float64):
        
        BaseEst.__init__(self, shape=shape, var_axes=var_axes, dtype=dtype,\
            name=name,type_name='BinaryQuantEst', nvars=1, cost_avail=True)
        self.y = y
        self.shape = shape
        self.thresh = thresh
        self.perr = perr
        self.cost_avail = True
        self.var_init = var_init 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def register(self, model):
		self.model = model
		if self.metric in ['auc', 'accuracy'] or model.direction=='max':
			self.monitor_op = np.greater
			self.best = -np.Inf
		else:
			self.monitor_op = np.less
			self.best = np.Inf 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def _validate_mab_args(arms, learning_policy, context_policy, seed, n_jobs, backend) -> NoReturn:
        """
        Validates arguments for the MAB constructor.
        """

        # Arms
        check_true(isinstance(arms, list), TypeError("The arms should be provided in a list."))
        check_true(len(arms) > 1, ValueError("The number of arms should be greater than 1."))
        check_false(None in arms, ValueError("The arm list cannot contain None."))
        check_false(np.nan in arms, ValueError("The arm list cannot contain NaN."))
        check_false(np.Inf in arms, ValueError("The arm list cannot contain Infinity."))
        check_true(len(arms) == len(set(arms)), ValueError("The list of arms cannot contain duplicate values."))

        # Learning Policy type
        check_true(isinstance(learning_policy,
                              (LearningPolicy.EpsilonGreedy, LearningPolicy.Popularity, LearningPolicy.Random,
                               LearningPolicy.Softmax, LearningPolicy.ThompsonSampling, LearningPolicy.UCB1,
                               LearningPolicy.LinTS, LearningPolicy.LinUCB)),
                   TypeError("Learning Policy type mismatch."))

        # Learning policy value
        learning_policy._validate()

        # Contextual Policy
        if context_policy:
            check_true(isinstance(context_policy,
                                  (NeighborhoodPolicy.KNearest, NeighborhoodPolicy.Radius,
                                   NeighborhoodPolicy.Clusters)),
                       TypeError("Context Policy type mismatch."))
            context_policy._validate()

        # Seed
        check_true(isinstance(seed, int), TypeError("The seed must be an integer."))

        # Parallel jobs
        check_true(isinstance(n_jobs, int), TypeError("Number of parallel jobs must be an integer."))
        check_true(n_jobs != 0, ValueError('Number of parallel jobs cannot be zero.'))
        if backend is not None:
            check_true(isinstance(backend, str), TypeError("Parallel backend must be a string.")) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def test_axis(self):
        # Vector norms.
        # Compare the use of `axis` with computing the norm of each row
        # or column separately.
        A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt)
        for order in [None, -1, 0, 1, 2, 3, np.Inf, -np.Inf]:
            expected0 = [norm(A[:, k], ord=order) for k in range(A.shape[1])]
            assert_almost_equal(norm(A, ord=order, axis=0), expected0)
            expected1 = [norm(A[k, :], ord=order) for k in range(A.shape[0])]
            assert_almost_equal(norm(A, ord=order, axis=1), expected1)

        # Matrix norms.
        B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)
        nd = B.ndim
        for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro']:
            for axis in itertools.combinations(range(-nd, nd), 2):
                row_axis, col_axis = axis
                if row_axis < 0:
                    row_axis += nd
                if col_axis < 0:
                    col_axis += nd
                if row_axis == col_axis:
                    assert_raises(ValueError, norm, B, ord=order, axis=axis)
                else:
                    n = norm(B, ord=order, axis=axis)

                    # The logic using k_index only works for nd = 3.
                    # This has to be changed if nd is increased.
                    k_index = nd - (row_axis + col_axis)
                    if row_axis < col_axis:
                        expected = [norm(B[:].take(k, axis=k_index), ord=order)
                                    for k in range(B.shape[k_index])]
                    else:
                        expected = [norm(B[:].take(k, axis=k_index).T, ord=order)
                                    for k in range(B.shape[k_index])]
                    assert_almost_equal(n, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import Inf [as 別名]
def test_keepdims(self):
        A = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)

        allclose_err = 'order {0}, axis = {1}'
        shape_err = 'Shape mismatch found {0}, expected {1}, order={2}, axis={3}'

        # check the order=None, axis=None case
        expected = norm(A, ord=None, axis=None)
        found = norm(A, ord=None, axis=None, keepdims=True)
        assert_allclose(np.squeeze(found), expected,
                        err_msg=allclose_err.format(None, None))
        expected_shape = (1, 1, 1)
        assert_(found.shape == expected_shape,
                shape_err.format(found.shape, expected_shape, None, None))

        # Vector norms.
        for order in [None, -1, 0, 1, 2, 3, np.Inf, -np.Inf]:
            for k in range(A.ndim):
                expected = norm(A, ord=order, axis=k)
                found = norm(A, ord=order, axis=k, keepdims=True)
                assert_allclose(np.squeeze(found), expected,
                                err_msg=allclose_err.format(order, k))
                expected_shape = list(A.shape)
                expected_shape[k] = 1
                expected_shape = tuple(expected_shape)
                assert_(found.shape == expected_shape,
                        shape_err.format(found.shape, expected_shape, order, k))

        # Matrix norms.
        for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro', 'nuc']:
            for k in itertools.permutations(range(A.ndim), 2):
                expected = norm(A, ord=order, axis=k)
                found = norm(A, ord=order, axis=k, keepdims=True)
                assert_allclose(np.squeeze(found), expected,
                                err_msg=allclose_err.format(order, k))
                expected_shape = list(A.shape)
                expected_shape[k[0]] = 1
                expected_shape[k[1]] = 1
                expected_shape = tuple(expected_shape)
                assert_(found.shape == expected_shape,
                        shape_err.format(found.shape, expected_shape, order, k))