Python numpy.less方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [as 別名]
def __init__(self, monitor='val_loss', 
                 min_delta=1e-6, patience=5,mode='min'):
#{{{
        super(EarlyStopping, self).__init__()

        self.monitor = monitor
        self.patience = patience
        self.min_delta = min_delta
        self.wait = 0
        self.stopped_epoch = 0
        self.stop_training=False;
        
        if mode =="min":
            self.monitor_op = np.less;
        elif mode == "max":
            self.monitor_op = np.greater;
        else:
            assert 0,"unknown early stop mode:";

        self.min_delta *= -1
#}}} 

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

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [as 別名]
def test_lesser():
    """Test for logical greater in onnx operators."""
    input1 = np.random.rand(1, 3, 4, 5).astype("float32")
    input2 = np.random.rand(1, 5).astype("float32")
    inputs = [helper.make_tensor_value_info("input1", TensorProto.FLOAT, shape=(1, 3, 4, 5)),
              helper.make_tensor_value_info("input2", TensorProto.FLOAT, shape=(1, 5))]

    outputs = [helper.make_tensor_value_info("output", TensorProto.FLOAT, shape=(1, 3, 4, 5))]

    nodes = [helper.make_node("Less", ["input1", "input2"], ["output"])]

    graph = helper.make_graph(nodes,
                              "lesser_test",
                              inputs,
                              outputs)

    greater_model = helper.make_model(graph)
    
    bkd_rep = mxnet_backend.prepare(greater_model)
    numpy_op = np.less(input1, input2).astype(np.float32)
    output = bkd_rep.run([input1, input2])
    npt.assert_almost_equal(output[0], numpy_op) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [as 別名]
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0):
  """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2.

  For each box in boxlist1, we want its IOA to be more than minoverlap with
  at least one of the boxes in boxlist2. If it does not, we remove it.

  Args:
    boxlist1: BoxList holding N boxes.
    boxlist2: BoxList holding M boxes.
    minoverlap: Minimum required overlap between boxes, to count them as
                overlapping.

  Returns:
    A pruned boxlist with size [N', 4].
  """
  intersection_over_area = ioa(boxlist2, boxlist1)  # [M, N] tensor
  intersection_over_area = np.amax(intersection_over_area, axis=0)  # [N] tensor
  keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap))
  keep_inds = np.nonzero(keep_bool)[0]
  new_boxlist1 = gather(boxlist1, keep_inds)
  return new_boxlist1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [as 別名]
def test_datetime_compare_nat(self):
        dt_nat = np.datetime64('NaT', 'D')
        dt_other = np.datetime64('2000-01-01')
        td_nat = np.timedelta64('NaT', 'h')
        td_other = np.timedelta64(1, 'h')

        for op in [np.equal, np.less, np.less_equal,
                   np.greater, np.greater_equal]:
            assert_(not op(dt_nat, dt_nat))
            assert_(not op(dt_nat, dt_other))
            assert_(not op(dt_other, dt_nat))

            assert_(not op(td_nat, td_nat))
            assert_(not op(td_nat, td_other))
            assert_(not op(td_other, td_nat))

        assert_(np.not_equal(dt_nat, dt_nat))
        assert_(np.not_equal(dt_nat, dt_other))
        assert_(np.not_equal(dt_other, dt_nat))

        assert_(np.not_equal(td_nat, td_nat))
        assert_(np.not_equal(td_nat, td_other))
        assert_(np.not_equal(td_other, td_nat)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [as 別名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [as 別名]
def argmin(self, axis=None, out=None):
        """Return indices of minimum elements along an axis.

        Implicit zero elements are also taken into account. If there are
        several minimum values, the index of the first occurrence is returned.

        Parameters
        ----------
        axis : {-2, -1, 0, 1, None}, optional
            Axis along which the argmin is computed. If None (default), index
            of the minimum element in the flatten data is returned.
        out : None, optional
            This argument is in the signature *solely* for NumPy
            compatibility reasons. Do not pass in anything except for
            the default value, as this argument is not used.

        Returns
        -------
         ind : np.matrix or int
            Indices of minimum elements. If matrix, its size along `axis` is 1.
        """
        return self._arg_min_or_max(axis, out, np.argmin, np.less) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [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 less [as 別名]
def test_minmax_func(self):
        # Tests minimum and maximum.
        (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
        # max doesn't work if shaped
        xr = np.ravel(x)
        xmr = ravel(xm)
        # following are true because of careful selection of data
        assert_equal(max(xr), maximum(xmr))
        assert_equal(min(xr), minimum(xmr))

        assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
        assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
        x = arange(5)
        y = arange(5) - 2
        x[3] = masked
        y[0] = masked
        assert_equal(minimum(x, y), where(less(x, y), x, y))
        assert_equal(maximum(x, y), where(greater(x, y), x, y))
        assert_(minimum(x) == 0)
        assert_(maximum(x) == 4)

        x = arange(4).reshape(2, 2)
        x[-1, -1] = masked
        assert_equal(maximum(x), 2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [as 別名]
def test_identity_equality_mismatch(self):
        a = np.array([np.nan], dtype=object)

        with warnings.catch_warnings():
            warnings.filterwarnings('always', '', FutureWarning)
            assert_warns(FutureWarning, np.equal, a, a)
            assert_warns(FutureWarning, np.not_equal, a, a)

        with warnings.catch_warnings():
            warnings.filterwarnings('error', '', FutureWarning)
            assert_raises(FutureWarning, np.equal, a, a)
            assert_raises(FutureWarning, np.not_equal, a, a)
            # And the other do not warn:
            with np.errstate(invalid='ignore'):
                np.less(a, a)
                np.greater(a, a)
                np.less_equal(a, a)
                np.greater_equal(a, a) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [as 別名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [as 別名]
def __gt__(self, other):
        if isinstance(other, Longitude):
            if self.hemisphere == 'W':
                if other.hemisphere == 'E':
                    return False
                else:
                    return self.longitude < other.longitude
            else:
                if other.hemisphere == 'W':
                    return True
                else:
                    return self.longitude > other.longitude
        else:
            return xr.apply_ufunc(np.less, other, self) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [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 less [as 別名]
def nan_compare(f, x, y, nan_nan=False, nan_val=False, val_nan=False):
    '''
    nan_compare(f, x, y) is equivalent to f(x, y), which is assumed to be a boolean function that
      broadcasts over x and y (such as numpy.less), except that NaN values in either x or y result
      in a value of False instead of being run through f.

    The argument f must be a numpy comparison function such as numpy.less that accepts the optional
    arguments where and out.

    The following optional arguments may be provided:
      * nan_nan (default: False) specifies the return value (True or False) for comparisons
        equivalent to f(nan, nan).
      * nan_val (default: False) specifies the return value (True or False) for comparisons
        equivalent to f(nan, non_nan).
      * val_nan (default: False) specifies the return value (True or False) for comparisons
        equivalent to f(non_nan, nan).
    '''
    #TODO: This should work with sparse matrices as well
    x = np.asanyarray(x)
    y = np.asanyarray(y)
    xii = np.isnan(x)
    yii = np.isnan(y)
    if not xii.any() and not yii.any(): return f(x, y)
    ii  = (~xii) & (~yii)
    out = np.zeros(ii.shape, dtype=np.bool)
    if nan_nan == nan_val and nan_val == val_nan:
        # All the nan-result values are the same; we can simplify a little...
        if nan_nan: out[~ii] = nan_nan
    else:
        if nan_nan: out[   xii &    yii] = nan_nan
        if nan_val: out[   xii & (~yii)] = nan_val
        if val_nan: out[(~xii) &    yii] = val_nan
    return f(x, y, out=out, where=ii) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import less [as 別名]
def nanlt(x, y, nan_nan=False, nan_val=False, val_nan=False):
    '''
    nanlt(x, y) is equivalent to (x < y) except that NaN values in either x or y result in False.

    The following optional arguments may be provided:
      * nan_nan (default: False) specifies the return value (True or False) for comparisons
        equivalent to nanlt(nan, nan).
      * nan_val (default: False) specifies the return value (True or False) for comparisons
        equivalent to nanlt(nan, 0).
      * val_nan (default: False) specifies the return value (True or False) for comparisons
        equivalent to nan;t(nan, 0).
    '''
    return nan_compare(np.less, x, y, nan_nan=nan_nan, nan_val=nan_val, val_nan=val_nan)