图像处理——线性内插..linear extrapolation 是线性外插法,也称线性外推。这种方法可用来研究随时间按恒定增长率变化的事物。在以时间为横坐标的坐标图中,事物的变化接近一条直线。根据这条直线,可以推断事物未来
Related to Linear extrapolation:Linear interpolation ex·trap·o·late (ĭk-străp′ə-lāt′) v.ex·trap·o·lat·ed,ex·trap·o·lat·ing,ex·trap·o·lates v.tr. 1.To infer or estimate by extending or projecting known information. ...
The interpolation and extrapolation problems can be regarded as a linear approximation problem on the unit disk in the complex plane. Although the robust one-step-ahead predictor and interpolator has already been considered separately in the previous literature, we give two conditions for the ...
pos_freqs=theta**(torch.arange(0,dim,2)[: (dim//2)].float()/dim) inv_freq_extrapolation=1.0/pos_freqs inv_freq_interpolation=1.0/(scale*pos_freqs) low=max(math.floor(dim*math.log(original_max_position_embeddings/(beta_fast*2*math.pi)))/(2*math.log(theta)),0) high=min(math.ce...
be used for interpolation. The most commonly used function for linear interpolation is theFORECASTfunction. For non-linear interpolation, Excel does not have a built-in function, but you can use other functions likeTREND,GROWTH, orLINESTto perform various types of curve fitting and extrapolation. ...
inv_freq_extrapolation = 1.0 / pos_freqs inv_freq_interpolation = 1.0 / (scale * pos_freqs) low = max(math.floor(dim * math.log(original_max_position_embeddings/(beta_fast * 2 * math.pi)))/(2 * math.log(theta)),0) high = min(math.ceil(dim * math.log(original_max_position_...
(device=pos_freqs.device)inv_freq_mask=1-ramp_funcinv_freq=inv_freq_interpolation* (1-inv_freq_mask) +inv_freq_extrapolation*inv_freq_mask_mscale=float((0.1*math.log(scale) +1.0) *mscale)t=torch.arange(max_position_embeddings, device=inv_freq.device, dtype=inv_freq.dtype) # torch....
Aerodynamic Forces Interpolation Extrapolation and Modeling of Unsteady Linear(ized) Aerodynamic ForcesInterpolation Extrapolation and Modeling of Unsteady Linear(ized) Aerodynamic ForcesGhiringhelli, Gian LucaMantegazza, Paolo
inv_freq_extrapolation = 1.0 / pos_freqs inv_freq_interpolation = 1.0 / (scale * pos_freqs) low = max(math.floor(dim * math.log(original_max_position_embeddings/(beta_fast * 2 * math.pi)))/(2 * math.log(theta)),0) high = min(math.ceil(dim * math.log(original_max_position_...
inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask _mscale = float((0.1 * math.log(scale) + 1.0) * mscale) t = torch.arange(max_position_embeddings, device=inv_freq.device, dtype=inv_freq.dtype) # torch.Size([2048]) ...