Python math.expm1() Examples
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Example #1
Source File: sync.py From pyclustering with GNU General Public License v3.0 | 6 votes |
def calculate_sync_order(oscillator_phases): """! @brief Calculates level of global synchronization (order parameter) for input phases. @details This parameter is tend 1.0 when the oscillatory network close to global synchronization and it tend to 0.0 when desynchronization is observed in the network. @param[in] oscillator_phases (list): List of oscillator phases that are used for level of global synchronization. @return (double) Level of global synchronization (order parameter). @see calculate_order_parameter() """ exp_amount = 0.0 average_phase = 0.0 for phase in oscillator_phases: exp_amount += math.expm1(abs(1j * phase)) average_phase += phase exp_amount /= len(oscillator_phases) average_phase = math.expm1(abs(1j * (average_phase / len(oscillator_phases)))) return abs(average_phase) / abs(exp_amount)
Example #2
Source File: MathTestCases.py From ufora with Apache License 2.0 | 5 votes |
def test_pure_python_math_module(self): vals = [1, -.5, 1.5, 0, 0.0, -2, -2.2, .2] # not being tested: math.asinh, math.atanh, math.lgamma, math.erfc, math.acos def f(): functions = [ math.sqrt, math.cos, math.sin, math.tan, math.asin, math.atan, math.acosh, math.cosh, math.sinh, math.tanh, math.ceil, math.erf, math.exp, math.expm1, math.factorial, math.floor, math.log, math.log10, math.log1p ] tr = [] for idx1 in range(len(vals)): v1 = vals[idx1] for funIdx in range(len(functions)): function = functions[funIdx] try: tr = tr + [function(v1)] except ValueError as ex: pass return tr r1 = self.evaluateWithExecutor(f) r2 = f() self.assertGreater(len(r1), 100) self.assertTrue(numpy.allclose(r1, r2, 1e-6))
Example #3
Source File: core.py From Gun-Detector with Apache License 2.0 | 5 votes |
def _log1mexp(x): """Numerically stable computation of log(1-exp(x)).""" if x < -1: return math.log1p(-math.exp(x)) elif x < 0: return math.log(-math.expm1(x)) elif x == 0: return -np.inf else: raise ValueError("Argument must be non-positive.")
Example #4
Source File: returns.py From backtrader with GNU General Public License v3.0 | 5 votes |
def stop(self): super(Returns, self).stop() if not self._fundmode: self._value_end = self.strategy.broker.getvalue() else: self._value_end = self.strategy.broker.fundvalue # Compound return try: nlrtot = self._value_end / self._value_start except ZeroDivisionError: rtot = float('-inf') else: if nlrtot < 0.0: rtot = float('-inf') else: rtot = math.log(nlrtot) self.rets['rtot'] = rtot # Average return self.rets['ravg'] = ravg = rtot / self._tcount # Annualized normalized return tann = self.p.tann or self._TANN.get(self.timeframe, None) if tann is None: tann = self._TANN.get(self.data._timeframe, 1.0) # assign default if ravg > float('-inf'): self.rets['rnorm'] = rnorm = math.expm1(ravg * tann) else: self.rets['rnorm'] = rnorm = ravg self.rets['rnorm100'] = rnorm * 100.0 # human readable %
Example #5
Source File: MathLib.py From PyFlow with Apache License 2.0 | 5 votes |
def expm1(x=('FloatPin', 0.1)): '''Return `e**x - 1`. For small floats `x`, the subtraction in `exp(x) - 1` can result in a significant loss of precision.''' return math.expm1(x)
Example #6
Source File: core.py From privacy with Apache License 2.0 | 5 votes |
def _log1mexp(x): """Numerically stable computation of log(1-exp(x)).""" if x < -1: return math.log1p(-math.exp(x)) elif x < 0: return math.log(-math.expm1(x)) elif x == 0: return -np.inf else: raise ValueError("Argument must be non-positive.")
Example #7
Source File: utils.py From mici with MIT License | 5 votes |
def log1m_exp(val): """Numerically stable implementation of `log(1 - exp(val))`.""" if val >= 0.: return nan elif val > LOG_2: return log(-expm1(val)) else: return log1p(-exp(val))
Example #8
Source File: core.py From models with Apache License 2.0 | 5 votes |
def _log1mexp(x): """Numerically stable computation of log(1-exp(x)).""" if x < -1: return math.log1p(-math.exp(x)) elif x < 0: return math.log(-math.expm1(x)) elif x == 0: return -np.inf else: raise ValueError("Argument must be non-positive.")
Example #9
Source File: sugar.py From hyper-engine with Apache License 2.0 | 5 votes |
def expm1(node): return merge([node], math.expm1)
Example #10
Source File: core.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
def _log1mexp(x): """Numerically stable computation of log(1-exp(x)).""" if x < -1: return math.log1p(-math.exp(x)) elif x < 0: return math.log(-math.expm1(x)) elif x == 0: return -np.inf else: raise ValueError("Argument must be non-positive.")