Python math.isnan方法代碼示例

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def _compute_delta(self, log_moments, eps):
    """Compute delta for given log_moments and eps.

    Args:
      log_moments: the log moments of privacy loss, in the form of pairs
        of (moment_order, log_moment)
      eps: the target epsilon.
    Returns:
      delta
    """
    min_delta = 1.0
    for moment_order, log_moment in log_moments:
      if math.isinf(log_moment) or math.isnan(log_moment):
        sys.stderr.write("The %d-th order is inf or Nan\n" % moment_order)
        continue
      if log_moment < moment_order * eps:
        min_delta = min(min_delta,
                        math.exp(log_moment - moment_order * eps))
    return min_delta 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def _compute_delta(log_moments, eps):
  """Compute delta for given log_moments and eps.

  Args:
    log_moments: the log moments of privacy loss, in the form of pairs
      of (moment_order, log_moment)
    eps: the target epsilon.
  Returns:
    delta
  """
  min_delta = 1.0
  for moment_order, log_moment in log_moments:
    if moment_order == 0:
      continue
    if math.isinf(log_moment) or math.isnan(log_moment):
      sys.stderr.write("The %d-th order is inf or Nan\n" % moment_order)
      continue
    if log_moment < moment_order * eps:
      min_delta = min(min_delta,
                      math.exp(log_moment - moment_order * eps))
  return min_delta 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def GenerateBinomialTable(m):
  """Generate binomial table.

  Args:
    m: the size of the table.
  Returns:
    A two dimensional array T where T[i][j] = (i choose j),
    for 0<= i, j <=m.
  """

  table = numpy.zeros((m + 1, m + 1), dtype=numpy.float64)
  for i in range(m + 1):
    table[i, 0] = 1
  for i in range(1, m + 1):
    for j in range(1, m + 1):
      v = table[i - 1, j] + table[i - 1, j -1]
      assert not math.isnan(v) and not math.isinf(v)
      table[i, j] = v
  return tf.convert_to_tensor(table) 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def backward(self, loss):
        '''
        Standard backward step with self.timer and debugger
        Arguments
            loss - the loss to perform loss.backward()
        '''
        self.timer.set()
        loss.backward()
        grad_norm = torch.nn.utils.clip_grad_norm_(
            self.model.parameters(), self.GRAD_CLIP)
        if math.isnan(grad_norm):
            self.verbose('Error : grad norm is NaN @ step '+str(self.step))
        else:
            self.optimizer.step()
        self.timer.cnt('bw')
        return grad_norm 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def write_log(self, log_name, log_dict):
        '''
        Write log to TensorBoard
            log_name  - <str> Name of tensorboard variable 
            log_value - <dict>/<array> Value of variable (e.g. dict of losses), passed if value = None
        '''
        if type(log_dict) is dict:
            log_dict = {key: val for key, val in log_dict.items() if (
                val is not None and not math.isnan(val))}
        if log_dict is None:
            pass
        elif len(log_dict) > 0:
            if 'align' in log_name or 'spec' in log_name:
                img, form = log_dict
                self.log.add_image(
                    log_name, img, global_step=self.step, dataformats=form)
            elif 'text' in log_name or 'hyp' in log_name:
                self.log.add_text(log_name, log_dict, self.step)
            else:
                self.log.add_scalars(log_name, log_dict, self.step) 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def from_float(cls, f):
        """Converts a finite float to a rational number, exactly.

        Beware that Fraction.from_float(0.3) != Fraction(3, 10).

        """
        if isinstance(f, numbers.Integral):
            return cls(f)
        elif not isinstance(f, float):
            raise TypeError("%s.from_float() only takes floats, not %r (%s)" %
                            (cls.__name__, f, type(f).__name__))
        if math.isnan(f):
            raise ValueError("Cannot convert %r to %s." % (f, cls.__name__))
        if math.isinf(f):
            raise OverflowError("Cannot convert %r to %s." % (f, cls.__name__))
        return cls(*f.as_integer_ratio()) 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def __eq__(a, b):
        """a == b"""
        if isinstance(b, numbers.Rational):
            return (a._numerator == b.numerator and
                    a._denominator == b.denominator)
        if isinstance(b, numbers.Complex) and b.imag == 0:
            b = b.real
        if isinstance(b, float):
            if math.isnan(b) or math.isinf(b):
                # comparisons with an infinity or nan should behave in
                # the same way for any finite a, so treat a as zero.
                return 0.0 == b
            else:
                return a == a.from_float(b)
        else:
            # Since a doesn't know how to compare with b, let's give b
            # a chance to compare itself with a.
            return NotImplemented 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def _richcmp(self, other, op):
        """Helper for comparison operators, for internal use only.

        Implement comparison between a Rational instance `self`, and
        either another Rational instance or a float `other`.  If
        `other` is not a Rational instance or a float, return
        NotImplemented. `op` should be one of the six standard
        comparison operators.

        """
        # convert other to a Rational instance where reasonable.
        if isinstance(other, numbers.Rational):
            return op(self._numerator * other.denominator,
                      self._denominator * other.numerator)
        if isinstance(other, float):
            if math.isnan(other) or math.isinf(other):
                return op(0.0, other)
            else:
                return op(self, self.from_float(other))
        else:
            return NotImplemented 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def draw_setpoint(self, *args):
        # draw a setpoint
        if self.setpoint_canvas:
            self.canvas.after.remove(self.setpoint_canvas)
            self.setpoint_canvas = None

        if math.isnan(self.setpoint_value) or math.isinf(self.setpoint_value):
            return

        v = self.value_to_angle(self.setpoint_value)
        length = self.dial_diameter / 2.0 - self.tic_length if not self.setpoint_length else self.setpoint_length
        self.setpoint_canvas = InstructionGroup()

        self.setpoint_canvas.add(PushMatrix())
        self.setpoint_canvas.add(Color(*self.setpoint_color))
        self.setpoint_canvas.add(Rotate(angle=v, axis=(0, 0, -1), origin=self.dial_center))
        self.setpoint_canvas.add(Translate(self.dial_center[0], self.dial_center[1]))
        self.setpoint_canvas.add(Line(points=[0, 0, 0, length], width=self.setpoint_thickness, cap='none'))
        # self.setpoint_canvas.add(SmoothLine(points=[0, 0, 0, length], width=self.setpoint_thickness))
        self.setpoint_canvas.add(PopMatrix())

        self.canvas.after.add(self.setpoint_canvas) 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def testParseFloat(self):
        engine, = PFAEngine.fromYaml('''
input: string
output: float
action: {parse.float: input}
''')
        self.assertEqual(engine.action("   123   "), 123.0)
        self.assertEqual(engine.action("   -123   "), -123.0)
        self.assertEqual(engine.action("   3.4028234e38   "), 3.4028234e38)
        self.assertEqual(engine.action("   -3.4028234e38   "), -3.4028234e38)
        self.assertEqual(engine.action("   3.4028236e38   "), float("inf"))
        self.assertEqual(engine.action("   -3.4028236e38   "), float("-inf"))
        self.assertEqual(engine.action("   1.4e-45   "), 1.4e-45)
        self.assertEqual(engine.action("   -1.4e-45   "), -1.4e-45)
        self.assertEqual(engine.action("   1e-46   "), 0.0)
        self.assertEqual(engine.action("   -1e-46   "), 0.0)
        self.assertTrue(math.isnan(engine.action("   nAN   ")))
        self.assertEqual(engine.action("   inf   "), float("inf"))
        self.assertEqual(engine.action("   +inf   "), float("inf"))
        self.assertEqual(engine.action("   -inf   "), float("-inf")) 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def testParseDouble(self):
        engine, = PFAEngine.fromYaml('''
input: string
output: double
action: {parse.double: input}
''')
        self.assertEqual(engine.action("   123   "), 123.0)
        self.assertEqual(engine.action("   -123   "), -123.0)
        self.assertEqual(engine.action("   1.7976931348623157e308   "), 1.7976931348623157e308)
        self.assertEqual(engine.action("   -1.7976931348623157e308   "), -1.7976931348623157e308)
        self.assertEqual(engine.action("   1.7976931348623159e308   "), float("inf"))
        self.assertEqual(engine.action("   -1.7976931348623159e308   "), float("-inf"))
        self.assertEqual(engine.action("   4.9e-324   "), 4.9e-324)
        self.assertEqual(engine.action("   -4.9e-324   "), -4.9e-324)
        self.assertEqual(engine.action("   1e-324   "), 0.0)
        self.assertEqual(engine.action("   1e-324   "), 0.0)
        self.assertTrue(math.isnan(engine.action("   nAN   ")))
        self.assertEqual(engine.action("   inf   "), float("inf"))
        self.assertEqual(engine.action("   +inf   "), float("inf"))
        self.assertEqual(engine.action("   -inf   "), float("-inf")) 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def __call__(self, state, scope, pos, paramTypes, x, *args):
        if len(args) == 3:
            numbins, low, high = args
            if low >= high or math.isnan(low) or math.isnan(high):
                raise PFARuntimeException("bad histogram range", self.errcodeBase + 0, self.name, pos)
            if numbins < 1:
                raise PFARuntimeException("bad histogram scale", self.errcodeBase + 1, self.name, pos)
            if math.isnan(x) or x < low or x >= high:
                raise PFARuntimeException("x out of range", self.errcodeBase + 2, self.name, pos)

            out = int(math.floor(numbins * div((x - low), (high - low))))

            if out < 0 or out >= numbins:
                raise PFARuntimeException("x out of range", self.errcodeBase + 2, self.name, pos)
            return out
        else:
            origin, width = args
            if math.isnan(origin) or math.isinf(origin):
                raise PFARuntimeException("bad histogram range", self.errcodeBase + 0, self.name, pos)
            if width <= 0.0 or math.isnan(width):
                raise PFARuntimeException("bad histogram scale", self.errcodeBase + 1, self.name, pos)
            if math.isnan(x) or math.isinf(x):
                raise PFARuntimeException("x out of range", self.errcodeBase + 2, self.name, pos)
            else:
                return int(math.floor(div((x - origin), width))) 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def __call__(self, state, scope, pos, paramTypes, str):
        try:
            out = float(str)
        except ValueError:
            raise PFARuntimeException("not a single-precision float", self.errcodeBase + 0, self.name, pos)
        if math.isnan(out):
            return out
        elif math.isinf(out):
            return out
        elif out > FLOAT_MAX_VALUE:
            return float("inf")
        elif -out > FLOAT_MAX_VALUE:
            return float("-inf")
        elif abs(out) < FLOAT_MIN_VALUE:
            return 0.0
        else:
            return out 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def __call__(self, state, scope, pos, paramTypes, x, y):
        if y == 0:
            if paramTypes[-1] == "int" or paramTypes[-1] == "long":
                raise PFARuntimeException("integer division by zero", self.errcodeBase + 0, self.name, pos)
            else:
                return float("nan")
        else:
            if not math.isnan(x) and not math.isinf(x) and math.isinf(y):
                return x
            else:
                out = x % y
                if x < 0 and out > 0:
                    return out - abs(y)
                elif x > 0 and out < 0:
                    return out + abs(y)
                else:
                    return out 

# 需要導入模塊: import math [as 別名]
# 或者: from math import isnan [as 別名]
def __call__(self, state, scope, pos, paramTypes, a, p):
        if len(a) == 0:
            raise PFARuntimeException("empty array", self.errcodeBase + 0, self.name, pos)
        if math.isnan(p):
            raise PFARuntimeException("p not a number", self.errcodeBase + 1, self.name, pos)
        if p <= 0.0:
            return lowestN(a, 1, lambda x, y: compare(jsonNodeToAvroType(paramTypes[0]).items, x, y) < 0)[0]
        if p >= 1.0:
            return highestN(a, 1, lambda x, y: compare(jsonNodeToAvroType(paramTypes[0]).items, x, y) < 0)[0]
        sa = sorted(a, lambda x, y: compare(jsonNodeToAvroType(paramTypes[0]).items, x, y))
        k = (len(a) - 1.0)*p
        f = math.floor(k)
        dataType = paramTypes[-1]
        if (dataType is "float") or (dataType is "double"):
            c = math.ceil(k)
            if f == c:
                return sa[int(k)]
            d0 = sa[int(f)] * (c - k)
            d1 = sa[int(c)] * (k - f)
            return d0 + d1
        else:
            if len(sa) % 2 == 1:
                return sa[int(f)]
            else:
                return sa[int(k)]