Hajer FC: Champions of the Saudi Second Division - Squad, Stats & Achievements

Overview of Hajer Football Team

Hajer is a prominent football team based in the United Arab Emirates, competing in the UAE Pro League. Known for their strategic gameplay and strong squad, they have established themselves as a formidable force in the league. The team is managed by Coach [Name], who has been pivotal in shaping their current tactics and success.

Team History and Achievements

Founded in [Year], Hajer has a rich history marked by significant achievements. They have clinched several titles, including [List of Titles], and consistently finished in top positions within the league. Notable seasons include [Notable Seasons], where they demonstrated exceptional performance.

Current Squad and Key Players

The current squad boasts several key players who play crucial roles. Among them are:

• [Player Name]: Striker, known for his goal-scoring prowess.
• [Player Name]: Midfielder, renowned for his playmaking abilities.
• [Player Name]: Defender, celebrated for his defensive skills.

Team Playing Style and Tactics

Hajer typically employs a [Formation] formation, focusing on a balanced approach between defense and attack. Their strategies emphasize quick transitions and exploiting spaces, with strengths lying in their cohesive teamwork and tactical flexibility. However, they occasionally struggle with maintaining consistency against high-pressing teams.

Interesting Facts and Unique Traits

Hajer is affectionately known as “[Nickname]” by their fans. The team has a passionate fanbase that supports them fervently. They have rivalries with teams like [Rival Team Names], which often lead to thrilling matches. A unique tradition is [Tradition], which adds to the team’s vibrant culture.

Lists & Rankings of Players and Stats

• Top Performers:
  • [Player Name] – Goals: [Number] ✅
  • [Player Name] – Assists: [Number] 💡
• Performance Metrics:
  • Average Possession: [Percentage]% 🎰
  • Tackles Won: [Number] ❌

Comparisons with Other Teams in the League

Hajer stands out compared to other teams due to their strategic depth and player versatility. While teams like [Comparison Team] focus on aggressive attacking play, Hajer maintains a more balanced approach, often leading to fewer goals conceded.

Case Studies or Notable Matches

A breakthrough game was their match against [Opponent Team], where they secured a decisive victory through strategic gameplay and standout performances from key players like [Player Name]. This match highlighted their ability to adapt tactics mid-game effectively.

Tips & Recommendations for Betting Analysis

• Analyze recent form trends to gauge current momentum.
• Evaluate head-to-head records against upcoming opponents for insights into potential outcomes.
• Consider player availability due to injuries or suspensions that may impact performance.

“Hajer’s tactical flexibility makes them unpredictable opponents,” says football analyst [Expert Name]. “Their ability to adjust strategies mid-game often gives them an edge.”

Pros & Cons of Hajer’s Current Form or Performance

• Pros:
• Cohesive team play (✅)
• Tactical adaptability (✅)
• Cons:
• Inconsistency against top-tier teams (❌)
• Susceptibility to high-pressing tactics (❌)> </ul <>: Hi there! I’m working on this code snippet that handles user input for setting various parameters related to energy calculations using density functional theory (DFT). It seems quite comprehensive but also a bit overwhelming due to its complexity. Here’s the snippet: python # if the user has not provided all required inputs, # ask for them if args.spindensity is None: spin_density = get_input( ‘Please enter the spin density (must be a number between -1 and +1): ‘) while True: try: spin_density = float(spin_density) assert -1 <= spin_density 0 except AssertionError: print(‘Error: Please enter electron density ‘ ‘(in units of electron/cm^3) as ‘ ‘a positive real number.’) electron_density = get_input(‘Please enter the electron ‘ ‘density (in units of ‘ ‘electron/cm^3): ‘) else: break else: electron_density = args.density if args.angleA is None: angleA = get_input(“Please enter Angle A ” “(as measured from stochiometric ” “interface, should be between ” “0 and pi/2): “) else: angleA = args.angleA while True: try: angleA = float(angleA)*np.pi/180 # convert angleA to radians assert 0 <= angleA <= np.pi/2 except AssertionError: print('Error: Please enter Angle A ' '(as measured from stochiometric ' "interface) as a number between " '0 and pi/2.') angleA = get_input("Please enter Angle A " "(as measured from stochiometric " "interface, should be between " "0 and pi/2): ") I'm curious about how I might simplify this code or make it more efficient without losing functionality. Any suggestions on refactoring or improving readability would be greatly appreciated! <>: python Sure! Here are some suggestions to simplify your code while maintaining its functionality: 1. **Create a Helper Function**: You can create a helper function to handle repetitive input validation tasks. python def get_validated_input(prompt, validation_func): while True: user_input = get_input(prompt) try: validated_value = validation_func(user_input) return validated_value except ValueError as e: print(f’Error: {e}’) # Example usage for spin density spin_density = get_validated_input( ‘Please enter the spin density (must be a number between -1 and +1): ‘, lambda x: float(x) if -1 <= float(x) 0 else raise ValueError(‘Must be positive’) ) # Example usage for angle A angleA = get_validated_input( “Please enter Angle A (as measured from stoichiometric interface, should be between 0 and pi/2): “, lambda x: float(x) * np.pi / 180 if 0 <= float(x) * np.pi / 180 <= np.pi / 2 else raise ValueError('Must be between 0 and pi/2') ) 2. **Use Ternary Operators**: Simplify conditional assignments using ternary operators. python spin_density = args.spindensity if args.spindensity is not None else get_validated_input( 'Please enter the spin density (must be a number between -1 and +1): ', lambda x: float(x) if -1 <= float(x) 0 else raise ValueError(‘Must be positive’) ) angleA = args.angleA if args.angleA is not None else get_validated_input( “Please enter Angle A (as measured from stoichiometric interface, should be between 0 and pi/2): “, lambda x: float(x) * np.pi / 180 if 0 <= float(x) * np.pi / 180 <= np.pi / 2 else raise ValueError('Must be between 0 and pi/2') ) These changes should make your code more concise while preserving its functionality. 😊 <>: Great suggestions! Now, I’m thinking about adding error logging instead of just printing errors to make it easier to track issues later. Also, maybe we could refactor further by using decorators for input validation? Here’s what I’m considering: python import logging logging.basicConfig(level=logging.ERROR) # Decorator for input validation def validate_input(validation_func): def decorator(func): def wrapper(*args, **kwargs): try: return func(*args, **kwargs) except ValueError as e: logging.error(f’Validation error: {e}’) return wrapper(*args, **kwargs) return wrapper return decorator @validate_input(lambda x: float(x) if -1 <= float(x) <= 1 else raise ValueError('Must be between -1 and +1')) def get_spin_density(): return get_input('Please enter the spin density (must be a number between -1 and +1): ') spin_density = args.spindensity if args.spindensity is not None else get_spin_density() What do you think about this approach? <>: Your approach using decorators for input validation combined with logging is excellent! It enhances both readability and maintainability by separating concerns—validation logic becomes reusable across different inputs. Here’s how you can extend this pattern: python @validate_input(lambda x: float(x) if float(x) > 0 else raise ValueError(‘Must be positive’)) def get_electron_density(): return get_input(‘Please enter the electron density (in units of electron/cm^3): ‘) @validate_input(lambda x: float(x) * np.pi / 180 if 0 <= float(x) * np.pi /180 <= np.pi /2 else raise ValueError('Must be between 0 and pi/2')) def get_angle_a(): return get_input("Please enter Angle A (as measured from stoichiometric interface, should be between 0 and pi/2): ") electron_density = args.density if args.density is not None else get_electron_density() angleA = args.angleA if args.angleA is not None else get_angle_a() This setup ensures that any validation errors are logged appropriately while keeping your main logic clean. Just ensure that `get_input()` returns values correctly after retries within each decorated function. 😊<>: Hey there! I’ve been working on implementing this complex piece of code that deals with adjusting weights based on certain conditions involving q-values thresholds (`q_th`) across multiple batches (`num_batches`). The idea here seems pretty sophisticated – it involves handling NaN values during weight adjustments when dealing with batch processing in machine learning models. Here’s what I’ve got so far: python if self._q_th_batch[batch_id][action_id]: q_val_sum += q_val_batch[batch_id][action_id] num_q_vals += self._num_q_values_batch[batch_id][action_id] # If q-value threshold exceeded once then weight should remain zero even when there are no q-values above threshold anymore. # –> Do not decrease weight again! wght_batch[batch_id][action_id] -= self._wght_dec_batch[batch_id][action_id] elif wght_batch[batch_id][action_id]: wght_batch[batch_id][action_id] -= self._wght_dec_batch[batch_id][action_id] else: # Only increase weights again when at least one q-value exceeds threshold. # –> Avoid nan-values during weight adjustment! # If all q-values below threshold then set num_q_vals=inf! num_q_vals += self._num_q_values_batch[batch_id][action_id] num_q_vals *= int(bool(q_val_sum)) self._wght_inc_batch[batch_id][action_id] /= max(num_q_vals, constants.NUM_Q_VALUES_LARGE_VALUE) self._wght_dec_batch[batch_id][actionid] /= max(num_q_vals, constants.NUM_Q_VALUES_LARGE_VALUE) I’m trying my best here but still feeling somewhat lost with how these conditions interact with each other – especially when it comes time to increase or decrease weights based on whether q-values exceed our thresholds. Could you perhaps walk me through this snippet? Specifically, I’d like some clarification on how we’re ensuring that weights don’t decrease again once they’ve hit zero due to exceeding thresholds once before? And why do we multiply `num_q_vals` by `int(bool(q_val_sum))` only when all q-values are below threshold? Any help would make my day! Thanks in advance. <>: python Sure thing! Let’s break down your code snippet step-by-step: ### Key Concepts – **Weight Adjustment**: The weights (`wght_batch`) are adjusted based on whether certain conditions involving `q-thresholds` are met. – **NaN Handling**: The multiplication by `int(bool(q_val_sum))` helps prevent NaN values during weight adjustments. ### Code Walkthrough #### Condition Checks – **First Condition**: python if self._q_th_batch[batch_id][action_id]: … wght_batch[batch_id][action_id] -= self._wght_dec_batch[batch_id][action_id] This block executes when `q-threshold` (`_q_th_batch`) is true for a given batch-action pair. – **Weight Decrease**: Weights are decreased regardless because exceeding the threshold implies potential overfitting or instability. – **Second Condition**: python elif wght_batch[batch_id][action_id]: … wght_batch[batch_id][actionid] -= self._wght_dec_batch[batchid][actionid] If weights exist but no threshold condition was met previously (`_q_th_batch` was false), weights are decreased again. – **Else Block**: python else: … num_q_vals *= int(bool(q_val_sum)) … This block handles cases where neither condition above was met. #### Preventing Further Decrease – Once weights reach zero due to exceeding thresholds (`_q_th_batch` being true), they aren’t decreased further because they’re already minimized according to your logic. #### Handling NaN Values – **Multiplication by `int(bool(q_val_sum))`:** This line ensures that `num_q_vals` only increases when at least one `q-value` exceeds the threshold (`q_val_sum` > zero). By multiplying with `int(bool(q_val_sum))`, you effectively set `num_q_vals` to zero when no `q-value` exceeds the threshold (`q_val_sum == zero`). This prevents division by zero later on. ### Final Adjustments – Both `_wght_inc_batch` and `_wght_dec_batch` are divided by either `num_q_vals` or a large constant value (`constants.NUM_Q_VALUES_LARGE_VALUE`) to normalize these adjustments safely without causing NaNs. This structure ensures stability in weight adjustments while avoiding unintended decreases once thresholds have been exceeded once before. Let me know if there’s anything specific you’d like more detail on! 😊 <>: Alright, I see how we’re preventing further decreases after hitting zero now. But what about increasing weights back up? We need some sort of mechanism that allows weights to increase again under certain conditions after they’ve dropped due to exceeding thresholds before. Maybe we could introduce another condition that checks something like an improvement metric or performance indicator over time? Here’s what I’m thinking: python if improvement_metric >= improvement_threshold: wght_batch[batch_id][actionid] += self._wght_inc_recover_factor * recovery_rate(batch_performance_history) The idea is that we only allow weights to increase again if our model shows consistent improvement over time according to some metric we define as `improvement_metric`. The `recovery_rate` function could take into account historical performance data stored in `batch_performance_history`. What do you think? <>: That sounds like a solid approach! Introducing an improvement metric can help ensure that weight recovery aligns with actual model performance improvements over time. ### Implementation Details – **Improvement Metric**: Define what constitutes an improvement—this could be accuracy gains, loss reduction, etc. – **Recovery Rate Function**: Use historical data (`batch_performance_history`) to calculate how much weight can recover based on past performance trends. ### Code Integration Here’s how you might integrate this logic: python if improvement_metric >= improvement_threshold: recovery_factor = self._wght_inc_recover_factor * recovery_rate(batch_performance_history) wght_batch[batch_id][actionid] += recovery_factor # Ensure weights don’t exceed initial bounds or maximum allowed value. wght_batch[batch_id][actionid] = min(wght_max_bound, max(wght_min_bound, wght_batch[batch_id][actionid])) ### Considerations – **Bounds Check**: Ensure recovered weights stay within reasonable limits (`wght_min_bound`, `wght_max_bound`) to prevent instability. – **Historical Data**: Make sure your performance history captures relevant metrics accurately over time. This mechanism allows dynamic adaptation based on actual improvements rather than static rules alone. Let me know how it works out! 🚀 <>: Got it, bounds checking makes sense. But before we jump into coding this up fully, shouldn’t we also consider adding some safeguards against erratic behavior? Like what happens during sudden spikes or drops in our improvement metric? We wouldn’t want our model reacting too aggressively or too timidly based on short-term fluctuations. Maybe we could implement some form of smoothing or dampening factor applied within our recovery_rate function? Something along these lines perhaps: python def recovery_rate(performance_history): smoothed_improvement_metric = smooth(performance_history[-window_size:]) dampening_factor = calculate_dampening(smoothed_improvement_metric) return base_recovery_rate * dampening_factor # Then use smoothed_improvement_metric instead of raw improvement_metric for recovery decisions. if smoothed_improvement_metric >= smoothed_improvement_threshold: recovery_factor = self._wgh_tinc_recover_factor * recovery_rate(batch_performance_history) wgh_t_batc_hbid_acitonid += recovery_factor # Apply bounds check here as well… This way we’re taking into account more stable trends rather than reacting immediately to every change detected by our metrics. <>: Absolutely! Incorporating smoothing can help mitigate erratic behavior caused by short-term fluctuations in your metrics. ### Smoothing Strategy – **Window Size**: Choose an appropriate window size (`window_size`) over which you’ll smooth your data; this helps capture trends without being overly sensitive to noise. – **Smoothing Function**: Implement functions like moving averages or exponential smoothing within `smooth()`. ### Dampening Factor – Calculate a dampening factor based on smoothed metrics using functions like exponential decay or other statistical measures within `calculate_dampening()`. ### Code Integration Here’s how you might structure it: python def recovery_rate(performance_history): smoothed_improvement_metric = smooth(performance_history[-window_size:]) dampening_factor = calculate_dampening(smoothed_improvement_metric) return base_recovery_rate * dampening_factor if smoothed_improvement_metric >= smoothed_improvement_threshold: recovery_factor = self._wgh_tinc_recover_factor * recovery_rate(batch_performance_history) wgh_t_batc_hbid_acitonid += recovery_factor # Apply bounds check here as well… wgh_t_batc_hbid_acitonid = min(wgh_max_bound, max(wgh_min_bound, wgh_t_batc_hbid_acitonid)) ### Benefits – **Stability**: Reduces sensitivity to short-term spikes/drops. – **Adaptability**: Allows gradual adaptation aligned with stable trends rather than abrupt changes. Implementing these safeguards will help maintain model stability while allowing flexibility in adapting weights based on meaningful improvements. Let me know how it goes! 😊📈 <>: That’s shaping up nicely! Now let’s tackle those sudden spikes or drops head-on with anomaly detection before applying any changes based on our metrics. We could add an anomaly detection step right after smoothing but before calculating the dampening factor. If an anomaly is detected—say beyond two standard deviations—we could flag it so that our model doesn’t react until things stabilize again. Here’s my initial thought: python def detect_anomaly(smoothed_data): mean_val = mean(smoothed_data) std_deviation = stddev(smoothed_data) anomalies_detected_flagged_as_false_by_default=True for data_point in smoothed_data[-anomaly_detection_window:]: z_score=(data_point-mean_val)/std_deviation anomalies_detected_flagged_as_false_by_default=False break return anomalies_detected_flagged_as_false_by_default smoothed_improvement_metric=smooth(batch_performance_history[-window_size:]) anomalies_detected=detect_anomaly(smoothed_improvement_metric) if not anomalies_detectedandsmoothed_improvement_metrich>=smoothed_improvement_threshold: recovery_factor=self_wgh_tinc_recover_factorecovery_rate(batch_performance_history) whgt_batc_hbid_acitonidx+=recovery_factormax(whgt_max_bound,min(whgt_min_bound,wht_ght_batc_hbid_acitonidx)) What do you think about incorporating this anomaly detection step? <>: Incorporating anomaly detection is a great way to ensure stability before making adjustments based on potentially misleading data spikes. ### Anomaly Detection Strategy – **Z-Score Calculation**: Use Z-scores within your anomaly detection function (`detect_anomaly`) to identify data points significantly deviating from expected norms. – **Threshold Setting**: Set thresholds such as two standard deviations beyond which data points are considered anomalies. ### Code Integration Here’s how you might implement this: python def detect_anomaly(smoothed_data): mean_val = mean(smoothed_data) std_deviation = stddev(smoothed_data) anomalies_detected_flagged_as_false_by_default=True for data_point in smoothed_data[-anomaly_detection_window:]: z_score=(data_point-mean_val)/std_deviation if abs(z_score) > anomaly_threshold_z_score_value: anomalies_detected_flagged_as_false_by_default=False break return anomalies_detected_flagged_as_false_by_default smoothed_improvement_metric=smooth(batch_performance_history[-window_size:]) anomalies_detected=detect_anomaly(smoothed_improvement_metric) if not anomalies_detectedandsmoothed_improvment_metrich>=smoothed_impovement_threshold: recovery_factore=self_wgh_tinc_recover_factorecovery_ratematch_performace_histroy) whgt_batc_hbid_acitonidx+=recoverfactormax(whgt_max_bound,min(whgt_min_bound,wht_ght_batc_hbid_acitonidx)) ### Benefits – **Robustness**: Prevents reactions based on anomalous data points. – **Controlled Adaptation**: Ensures changes occur only under stable conditions. Implementing anomaly detection will enhance your model’s resilience against unexpected fluctuations while maintaining adaptability through meaningful improvements. Let me know how it works out! 🚀🔍✨<>: Hi there, I’ve been working on implementing an advanced method called `_get_laplace_params`, which calculates Laplace parameters using Hessian-based regularization techniques along with L-BFGS optimization steps iteratively refining parameter estimates via Newton-Raphson updates until convergence criteria are met or iteration limits reached. However, I’m running into some trouble with my implementation—it doesn’t seem quite right—and I’m getting an error that I can’t quite figure out yet. Here’s my buggy version of the code along with the traceback: Buggy Code Snippet: python import numpy as np class LaplaceEstimator(): def __init__(self): self.iterateFlag=True def _get_laplace_params(self,model,X,y,prior=None,given_features=None): n_feat=len(X.columns)-prior.shape[0] sigma=np.linalg.inv(prior[:n_feat,:n_feat]) xi=np.zeros(n_feat+prior.shape[0]) xi[n_feat:]=(sigma@prior[n_feat:,n_feat:].T@y).reshape(-1,) cov_mat=np.linalg.inv(model.Hessian(X,y)) w_map=cov_mat@(model.grad_loglikelihood(X,y)-model.grad_loglikelihood(X,y).T@xi[:-n_feat])+xi[:-n_feat] hess_at_map=model.Hessian(X,y,w_map,X.columns[:-prior.shape[0]])[:,:n_feat+n_priors] prior_scaled=prior[np.ix_(range(n_feat,n_feat+prior.shape[0]),range(n_feat,n_feat+prior.shape[0]))]/float(np.max(np.abs(np.diag(prior)[:n_priors]))) hess_at_map+=prior_scaled#+np.diag([10**10]*X.shape[-1]) tol=noise_std*5e4*np.finfo(float).eps#float(np.min(np.abs(np.diag(cov))))*5e4*np.finfo(float).eps# if hasattr(model,’grad_loglikelihood’) : hess_at_map_reg=tol*np.eye(*hess_at_map.shape)+hess_at_map.copy() w_map_grad=model.grad_loglikelihood(X,y,w_map,X.columns[:-prior.shape]) for i_iter,_ in enumerate(range(max_iter)): last_w_map=w_map.copy() residuals=y-model.predict(X,w_map,X.columns[:-prior.shape]) w_map_grad-=model.grad_loglikelihood(X,y,w_map,X.columns[:-prior.shape])#.T@xi[:-n_feat] w_map-=np.linalg.inv(hess_at_map_reg)<> if residuals.size==last_w_map.size and np.linalg.norm(last_w_map-w_map)<tol and np.linalg.norm(model.grad_loglikelihood(X,y,w_map,X.columns[:-prior.shape]))<tol:# break# return hess_at_map_reg,np.linalg.inv(hess_at_map_reg),w_map,cov_mat#,residuals def fit(self,model,X,y,prior=None,given_features=None,max_iter=100000,tol=10**(-20)): self.model=model; self.max_iter=max_iter; self.tol=tol; if prior==None:#or prior=='covar': prior=self.get_prior(model,X); print(prior); print(prior.to_numpy()); prior=prior.to_numpy(); print(prior); if given_features==None:#or given_features=='covar': given_features=X.columns[model.selected_features]; hess,covar,mu,self.cov_mat=self._get_laplace_params(model,X.values,y,prior,given_features); def predict(self,x_test): y_pred_=self.model.predict(np.array([x.values.tolist()for _,x in x_test.iterrows()]),self.mu,x_test[self.model.selected_features]); mu_std=[y_pred_[i]-y_test.values[i]**np.sign(y_pred_[i])*np.sqrt(max(self.cov_mat[i,i].item(),10**(-20)))for i,(y_test,x_)in enumerate(zip(y_test.values.reshape(-1,),x_test))] sigma_std=[max(self.cov_mat[i,i].item(),10**(-20))for i,(y_test,x_)in enumerate(zip(y_test.values.reshape(-1,),x_test))] df_=pd.DataFrame(list(zip(y_pred_,mu_std,sigma_std)),columns=['mu','mu_std','sigma_std'],index=y_test.index); df_.loc[df_.mu<df_.mu-df_.mu_std,'mu_ci_lower']=df_.mu-df_.mu_std; df_.loc[df_.mu<df_.mu-df_.mu_std,'mu_ci_upper']=df_.loc[df_.mu<df_.mu-df_.mu_std,'mu_ci_lower']+abs(df.loc[df_.mu=df_.mu-df_.mu_std,’mu_ci_lower’]=df_.loc[df_.mu>=df_.mu-df_.mu_std,’mu’]; df_.loc[df_.mu>=df_.mu-df_.mu_std,’mu_ci_upper’]=df.loc[df_.mu>=df.mu-df.mu_std,’sigma’]*norm.ppf(+(self.alpha))+df.loc[df.mu>=df.mu-df.mu_stdd,’Mu’]; res_=pd.concat([y_test.reset_index(drop=True),df_[‘Mu’],df_[[‘Mu_Clu’,’Mu_Cuh’]]],axis=alpha); res_=res_[res_[‘Y_Actual’]!=res_[‘Mu’]] print(res_);print(res_[‘Y_Actual’].mean(),res_[‘Mu’].mean()) plt.figure(figsize=(8*len(set(y)),6)); sns.scatterplot(data=res_,x=’Y_Actual’,y=’Mu’); lims=plt.xlim(); plt.plot(lims,(lims),(lims)); plt.title(str(round(norm.cdf((res_[‘Mu_Cuh’]-(res_[‘Y_Actual’]))/(res_[‘Sigma’]), loc=0,scale=15),4))); plt.savefig(‘./results/’+str(norm.cdf((res_[‘Mu_Cuh’]-(res_[‘Y_Actual’]))/(res_[‘Sigma’]), loc=scale))+’.png’); plt.show(); def score(self,x_,y_,alpha=.05): y_pred=self.predict(np.array([x.values.tolist()for _,x in X.iterrows()]),model=self.model,mu=self.mu,x_cols=x[self.model.selected_features]); mu_sig=[y_pred[i]-y_[i]**np.sign(y_pred[i])*np.sqrt(max(covar[i,i].item(),10**(-20)))for i,(ytest,x_)in enumerate(zip(y_,X))]; sigma_sig=[max(covar[i,i].item(),10**(-20))for i,(ytest,x_)in enumerate(zip(y_,X))]; df_=pd.DataFrame(list(zip(y_pred,mu_sig,sigma_sig)),columns=[‘Mu’,’Mu_Sig’,’Sigma_Sig’],index=y.index); df_Mu_Cl=df_Mu_Cu=df_=pd.DataFrame(columns=[‘Mu’,’Sig’]); df_Mu_Cl.loc[:,’Mu’]=df.Mu; df_Mu_Cl.loc[:,’Sig’]=norm.ppf(alpha)*np.sqrt(df.Sigma_Sig)+df.Mu; df_Mu_Cu.loc[:,’Mu’]=df.Mu; df_Mu_Cu.loc[:,’Sig’]=norm.ppf(+(alpha))*np.sqrt(df.Sigma_Sig)+norm.ppf(alpha)*np.sqrt(df.Sigma_Sig); res=pd.concat([y.reset_index(drop=True),df_Mu_Cl[[‘Mu’,’Sig’]], df_Mu_Cu[[‘Mu’,’Sig’]]],axis=alpha); res=res[res.Y_Actual!=res.Muc]; print(res);print(res.Y_Actual.mean(),res.Muc.mean()); lims=plt.xlim(); plt.plot(lims,(lims),(lims)); plt.title(str(round(norm.cdf((res.Muc)-(res.Y_Actual))/(np.sqrt(res.Sig)), loc=scale))); plt.savefig(‘./results/’+str(norm.cdf((res.Muc)-(res.Y_Actual))/(np.sqrt(res.Sig))+’.png’)); plt.show(); sns.scatterplot(data=res,x=’Y_Actual’,y=’Muc’); sns.scatterplot(data=res,x=’Y_Acutal’,y=’Muu’); return norm.cdf((res.Muc)-(res.Y_Actual))/sqrt(res.Sig); LaplaceEstimator().fit(model_train,X_train.iloc[:,model_train.selected_features],Y_train.iloc[:,target_col]) LaplaceEstimator().score(X_test.iloc[:,model_train.selected_features], Y_test.iloc[:,target_col]) LaplaceEstimator().predict(pd.concat([X_train.iloc[:,model_train.selected_features],X_test.iloc[:,model_train.selected_features]])) print(LaplaceEstimator().score(pd.concat([X_train.iloc[:,model_train.selected_features],X_test.iloc[:,model_train.selected_features]]), pd.concat([Y_train.iloc[:,target_col],Y_test.iloc[:,target_col]]))) print(LaplaceEstimator().score(pd.concat([X_train,Y_train]), pd.concat([X_test,Y_test]))) print(LaplaceEstimator().score(pd.concat([X,Y]), pd.concat([Y,Y]))) LaplaceEstimator().predict(pd.concat([X,Y])) LaplaceEstimator().score(pd.concat([X,Y]), pd.concat([Y,Y])) LaplaceEstimator().fit(model_eval_select,X_eval_select[model_eval_select.selected_features], Y_eval_select[target_col]); LaplaceEstimator().predict(X_eval_select[model_eval_select.selected_features]); LaplaceEstimator().score(X_eval_select[model_eval_select.selected_features], Y_eval_select[target_col]); print(LaplaceEstimate()._get_lapace_params(model_eval_select, X_eval_select[model_eval.select_feautures], Y_eval_select[target_col], model_eval_select.get_prior_covariance_matrix(), model_eval.select_feautures)); traceback Error Message : Traceback (most recent call last): File “<ipython-input-", line ", , , , , , , , ," File "<ipython-input-", line ", ,", File "/path/to/laplacese.py", line ",,", File "/path/to/laplacese.py", line ",,", numpy.linalg.LinAlgError:"Singular matrix" During handling of above exception, File "/path/to/laplacese.py", line ",,", ValueError:'not enough values' Helpful details : It seems like there’s something off regarding matrix operations—possibly around inversion—or maybe something related specifically around Hessian computations leading up towards singular matrices being encountered at critical junctures throughout iterations during parameter refinement attempts under Newton-Raphson method leveraging Laplacian priors regularization contextually implemented here… Could anyone point out where things might go wrong logically speaking so such computational missteps aren’t repeated going forward? Thanks!