Conan: What's Wrong with My Style of Painting Yan Shuangying?
by Style Is The Most Important
About This Novel
"He's coming." "I don't think I need to name the person, that would be redundant." "He is terrifying! More terrifying than any killer or criminal that has ever appeared in Mika Town. He is not a human, he is a ghost!" Many criminals gathered together, and one criminal who had seen the half-human, half-ghost figure spoke with a trembling voice. The next moment, a homemade explosive rolled into the room. Boom! The man in black lit a cigarette and left calmly. "In this world, good people will not die, and neither will bad people. Only one kind of people will die - stupid people!" The Sherlock Holmes of the Heisei era couldn't save this city, what it needs is the Yan Shuangying of the Heisei era! ------ Edogawa Conan [Where did this terrifying black-clad double-gun man with a wrong style of painting come from? Black organization? Vodka [This is the first time I saw someone flying out on the airwaves of an exploding train. He was simply Superman! Jiangu LingDo you want to compete with me for the position of working emperor? Hikaru Nara [Anyone who commits a crime in Yoneka Town will see his or her own brains... I bet an ocean that there are no bullets in your gun!
What Readers Think
Rating
Community(0)
Official(19)Scraped 6d ago
Great, it's Yan Shuangying, we are saved
Great, thank you, Yan Shuangying is hopeless.
Just reading the first few chapters, I was immediately persuaded to quit, just give Yan Shuangying the nanny Ying.
Anyone who has seen it can tell me whether it is mainly about solving crimes or something else, whether there is a female protagonist, and is she a Conan nanny?
I have read Chapter 242 so far. My thoughts: This TM looks more and more like Gotham and Batman😂, Crazy city, organizations with hands and eyes, endless criminals; The man who returns from death comes and goes without a trace; The city did not become better after killing its greatest enemy; Iconic cars, discover the magic hidden in the world on the technological side; And logistics, and imitators, the Bat Family, right😂. Not to mention the no-kill principle, this is Gotham and Batman!
It's repeated too much and I'm tired of hearing it.
Have you seen any female protagonists?
It looks good. It would be perfect if it were faster.
Yan Shuangying's daily routine feels more difficult to handle
% Assume that the EEG data eeg_data has been loaded % Define the model order range p_max = 20; p_range = 1: p_max; % Initialize FPE and AIC vectors FPE_values = zeros(1, p_max); AIC_values = zeros(1, p_max); % Calculate FPE and AIC under different orders for p = p_range % Use the aryule function to obtain the AR model coefficients and noise variance [ar_coeffs, NoiseVariance] = aryule(eeg_data, p); N = length(eeg_data); % Calculate FPE FPE_values(p) = NoiseVariance * ((N + p + 1) / (N - p - 1)); % Calculate AIC AIC_values(p) = N * log(NoiseVariance) + 2 * p; end % Select the optimal order according to the FPE criterion [min_FPE, best_p_FPE] = min(FPE_values); fprintf('According to the FPE criterion, the optimal order is: %d ', best_p_FPE); % Select the optimal order according to the AIC criterion [min_AIC, best_p_AIC] = min(AIC_values); fprintf('According to the AIC criterion, the optimal order is: %d ', best_p_AIC); % Draw a graph of FPE changing with order figure; plot(p_range, FPE_values); title('Final prediction error criterion (FPE) changes with the AR model order'); xlabel('AR model order'); ylabel('FPE value'); hold on; plot(best_p_FPE, min_FPE, 'ro', 'MarkerSize', 8, 'DisplayName', 'optimal order'); legend; % Draw a graph of AIC changing with order figure; plot(p_range, AIC_values); title('The information theory criterion (AIC) changes with the order of the AR model'); xlabel('AR model order'); ylabel('AIC value'); hold on; plot(best_p_AIC, min_AIC, 'ro', 'MarkerSize', 8, 'DisplayName', 'optimal order'); legend;
Rating
Community(0)
Official(19)Scraped 6d ago
Great, it's Yan Shuangying, we are saved
Great, thank you, Yan Shuangying is hopeless.
Just reading the first few chapters, I was immediately persuaded to quit, just give Yan Shuangying the nanny Ying.
Anyone who has seen it can tell me whether it is mainly about solving crimes or something else, whether there is a female protagonist, and is she a Conan nanny?
I have read Chapter 242 so far. My thoughts: This TM looks more and more like Gotham and Batman😂, Crazy city, organizations with hands and eyes, endless criminals; The man who returns from death comes and goes without a trace; The city did not become better after killing its greatest enemy; Iconic cars, discover the magic hidden in the world on the technological side; And logistics, and imitators, the Bat Family, right😂. Not to mention the no-kill principle, this is Gotham and Batman!
It's repeated too much and I'm tired of hearing it.
Have you seen any female protagonists?
It looks good. It would be perfect if it were faster.
Yan Shuangying's daily routine feels more difficult to handle
% Assume that the EEG data eeg_data has been loaded % Define the model order range p_max = 20; p_range = 1: p_max; % Initialize FPE and AIC vectors FPE_values = zeros(1, p_max); AIC_values = zeros(1, p_max); % Calculate FPE and AIC under different orders for p = p_range % Use the aryule function to obtain the AR model coefficients and noise variance [ar_coeffs, NoiseVariance] = aryule(eeg_data, p); N = length(eeg_data); % Calculate FPE FPE_values(p) = NoiseVariance * ((N + p + 1) / (N - p - 1)); % Calculate AIC AIC_values(p) = N * log(NoiseVariance) + 2 * p; end % Select the optimal order according to the FPE criterion [min_FPE, best_p_FPE] = min(FPE_values); fprintf('According to the FPE criterion, the optimal order is: %d ', best_p_FPE); % Select the optimal order according to the AIC criterion [min_AIC, best_p_AIC] = min(AIC_values); fprintf('According to the AIC criterion, the optimal order is: %d ', best_p_AIC); % Draw a graph of FPE changing with order figure; plot(p_range, FPE_values); title('Final prediction error criterion (FPE) changes with the AR model order'); xlabel('AR model order'); ylabel('FPE value'); hold on; plot(best_p_FPE, min_FPE, 'ro', 'MarkerSize', 8, 'DisplayName', 'optimal order'); legend; % Draw a graph of AIC changing with order figure; plot(p_range, AIC_values); title('The information theory criterion (AIC) changes with the order of the AR model'); xlabel('AR model order'); ylabel('AIC value'); hold on; plot(best_p_AIC, min_AIC, 'ro', 'MarkerSize', 8, 'DisplayName', 'optimal order'); legend;
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