Searched refs:frac (Results 1 – 10 of 10) sorted by relevance
54 float frac = 3.0f; in HWTEST_F() local58 rsPathAnimation.SetEndFraction(frac); in HWTEST_F()72 float frac = 3.0f; in HWTEST_F() local74 rsPathAnimation.SetEndFraction(frac); in HWTEST_F()125 float frac = 3.0f; in HWTEST_F() local129 rsPathAnimation.SetBeginFraction(frac); in HWTEST_F()145 float frac = 3.0f; in HWTEST_F() local147 rsPathAnimation.SetBeginFraction(frac); in HWTEST_F()
236 float frac = 3.f; in HWTEST_F() local237 rsAnimation.SetFraction(frac); in HWTEST_F()249 float frac = 0.0f; in HWTEST_F() local252 rsAnimation.SetFraction(frac); in HWTEST_F()
13 $ \text{q}(x_i) = clamp(round(\frac{r}{scale}+zeroPoint), min , max) $19 $ scale = \frac{r_{max}-r_{min}}{q_{max}-q_{min}} $22 $ zeroPoint = round(q_{min}-\frac{r_{min}}{scale}) $
134 …moid激活函数。<br/>按元素计算Sigmoid激活函数。Sigmoid函数定义为：<br/>$ \text{Sigmoid}(x_i) = \frac{1}{1 + \exp(-x_i)} …140 | ACTIVATION_TYPE_SOFTSIGN | SoftSign激活函数。<br/>SoftSign函数定义如下：<br/>$ \text{SoftSign}(x_i) = \frac{x…142 … | Tanh激活函数。<br/>Tanh函数定义如下：<br/>$ tanh(x) = \frac{\exp(x_i) - \exp(-x_i)}{\exp(x_i) + \exp(-x_i)}…144 …wish激活函数。<br/>Hard Swish函数定义如下：<br/>$ \text{Hardswish}(x_{i}) = x_{i} \* \frac{ReLU6(x_{i} + 3)}{6…145 …<br/>Hard Sigmoid函数定义如下：<br/>$ \text{Hardsigmoid}(x_{i}) = max(0, min(1, \frac{x_{i} + 3}{6})) $<b…542 …frac{x_2-x}{x_2-x_1}f(Q_{11})+\frac{x-x_1}{x_2-x_1}f(Q_{21}) $<br/>$ f(x,y_2) = \frac{x_2-x}{x_2-x…
133 …| Sigmoid激活函数。按元素计算Sigmoid激活函数。Sigmoid函数定义为：<br/>$ \text{Sigmoid}(x_i) = \frac{1}{1 + \exp(-x_i)} …139 | ACTIVATION_TYPE_SOFTSIGN | SoftSign激活函数。SoftSign函数定义如下：<br/>$ \text{SoftSign}(x_i) = \frac{x_i}{1…141 …_TANH | Tanh激活函数。Tanh函数定义如下：<br/>$ tanh(x) = \frac{\exp(x_i) - \exp(-x_i)}{\exp(x_i) + \exp(-x_i)}…143 | ACTIVATION_TYPE_HSWISH | Hard Swish激活函数。<br/>$ \text{Hardswish}(x_{i}) = x_{i} \* \frac{ReLU6(x_{…144 …活函数。 Hard Sigmoid函数定义如下：<br/>$ \text{Hardsigmoid}(x_{i}) = max(0, min(1, \frac{x_{i} + 3}{6})) $<b…590 …frac{x_2-x}{x_2-x_1}f(Q_{11})+\frac{x-x_1}{x_2-x_1}f(Q_{21}) $<br/>$ f(x,y_2) = \frac{x_2-x}{x_2-x…
306 float frac = std::stof(output[1]); in SetProgress() local308 if (frac >= -EPSINON && frac <= EPSINON) { in SetProgress()311 tmpProgressValue = static_cast<int>(frac * g_percentage); in SetProgress()313 if (frac >= FULL_EPSINON && g_tmpValue + g_percentage < FULL_PERCENT_PROGRESS) { in SetProgress()353 float frac; in HandleChildOutput() local358 frac = std::stof(progress[0]); in HandleChildOutput()359 g_percentage = static_cast<int>(frac * FULL_PERCENT_PROGRESS); in HandleChildOutput()
181 \text{q}(x_i) = clamp(round(\frac{r}{scale}+zeroPoint), min , max)189 scale = \frac{r_{max}-r_{min}}{q_{max}-q_{min}}193 zeroPoint = round(q_{min}-\frac{r_{min}}{scale})505 f(x,y_1) = \frac{x_2-x}{x_2-x_1}f(Q_{11})+\frac{x-x_1}{x_2-x_1}f(Q_{21})509 f(x,y_2) = \frac{x_2-x}{x_2-x_1}f(Q_{12})+\frac{x-x_1}{x_2-x_1}f(Q_{22})513 f(x,y) = \frac{y_2-y}{y_2-y_1}f(x,y_1)+\frac{y-y_1}{y_2-y_1}f(x,y_2)617 \text{Sigmoid}(x_i) = \frac{1}{1 + \exp(-x_i)}684 \text{SoftSign}(x_i) = \frac{x_i}{1 + |x_i|}703 …tanh(x) = \frac{\exp(x_i) - \exp(-x_i)}{\exp(x_i) + \exp(-x_i)} = \frac{\exp(2x_i) - 1}{\exp(2x_i)…727 \text{Hardswish}(x_{i}) = x_{i} * \frac{ReLU6(x_{i} + 3)}{6}[all …]
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