12/31/2023 0 Comments Linspace matlab![]() %%% Adjusts viewing frame and creates optimized alpha and beta parameters Max_bro = betas(x0)+(beta_range./(0.5)) %Creates optimized minimum of beta range half distance away from identified optimal beta value from previous iteration. If min_bro < 0 % Conditional statement if minimum beta range is less than zero min_bro = 0 Min_bro = betas(x0)-(beta_range./(0.5)) %Creates optimized minimum of beta range half distance away from identified optimal beta value from previous iteration. Max_aro = alphas(y0)+(alpha_range./(0.5)) %Creates optimized maximum value of alpha range half distance away from identified optimal alpha value from previous iteration.īeta_range = beta_range.*0.7 % Decreases beta range by 30% If min_aro < 0 % Conditional statement if minimum alpha range is less than zero min_aro = 0 ![]() Min_aro = alphas(y0)-(alpha_range./(0.5)) %Creates optimized minimum of alpha range half distance away from identified optimal alpha value from previous iteration. %%% Identifies alpha and beta values that correspond to the coordinates forĪlpha_range = alpha_range.*0.7 % Decreases alpha range by 30% ![]() = find(all_SSD_cond0 = min(min(all_SSD_cond0))) Could someone help me to better understand where I'm making my error and how to correct this?Īlphas = linspace(min_aro, max_aro,10) " %Finds coordinates of optimal parameters for Condition 0 I'm assuming it has to do with the " if min_ < 0." addition. If I'm understanding the problem correctly its becuase I'm passing a vector into the linspace function instead of a scalar, but I don't understand why that is occuring now and was not the case with my previous adaptation. Once I edited my script to include a conditional "If" section to prevent parameters from being less than zero, I encountered the "Error using linspace (line 22) Inputs must be scalar. Initially using a prior adaptation I had no issues. I'm using linspace to generate new values to test against the participant data. Within the model, I identify the optimal parameter values that best fit participant data then adjust and reduce the viewing window (range) to get more precise parameter values. I'm working on fitting a model to data I have collected.
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