Araz PFK: Premier League of Azerbaijan Squad and Achievements Overview

Overview of Araz PFK

Araz PFK is a prominent football club based in the city of Ganja, Azerbaijan. Competing in the Azerbaijan Premier League, the team was founded in 1993. The club is currently managed by Coach Anar Nazirov and plays its home games at the Ganja City Stadium.

Team History and Achievements

Araz PFK has a rich history with several notable achievements. The team has won the Azerbaijan Cup multiple times and has been a consistent contender in the Azerbaijan Premier League. Notable seasons include their championship wins and strong performances that have placed them among the top teams in the league.

Current Squad and Key Players

The current squad features several standout players, including star striker Vugar Gurbanov and key midfielder Ramil Sheydayev. These players are crucial to the team’s attacking prowess, with Gurbanov known for his goal-scoring ability and Sheydayev for his playmaking skills.

Team Playing Style and Tactics

Araz PFK typically employs a 4-3-3 formation, focusing on a balanced approach between defense and attack. Their strategy emphasizes quick transitions and utilizing wide areas to create scoring opportunities. Strengths include their solid defensive line and dynamic forward play, while weaknesses may involve occasional lapses in midfield control.

Interesting Facts and Unique Traits

The team is affectionately known as “The Lions” due to their fierce playing style. Araz PFK boasts a passionate fanbase that supports them through thick and thin. Rivalries with teams like Qarabağ FC add an extra layer of excitement to their matches, while traditions such as pre-match rituals enhance fan engagement.

Lists & Rankings of Players, Stats, or Performance Metrics

• Vugar Gurbanov: Top scorer ✅
• Ramil Sheydayev: Playmaker 🎰
• Defensive Record: Strongest in league 💡

Comparisons with Other Teams in the League

Araz PFK is often compared to other top-tier teams like Qarabağ FC and Neftçi Baku FK. While Araz excels in tactical discipline, Qarabağ is known for its attacking flair, making matches between these teams highly anticipated by fans.

Case Studies or Notable Matches

One of Araz PFK’s breakthrough games was their victory against Neftçi Baku FK in 2019, which secured their position as league leaders for that season. This match showcased their strategic prowess and ability to perform under pressure.

Tips & Recommendations for Betting Analysis

• Analyze recent form trends to gauge current performance levels.
• Evaluate head-to-head records for insights into rivalry dynamics.
• Consider player injuries or suspensions that might impact game outcomes.

“Araz PFK’s tactical discipline makes them formidable opponents on any given day,” says sports analyst Alexei Petrov.

Pros & Cons of the Team’s Current Form or Performance

• ✅ Pro: Strong defensive record provides stability.
• ❌ Con: Midfield vulnerabilities can be exploited by opponents.

Frequently Asked Questions about Araz PFK Betting Analysis

<>: Hi there! I’m working on this code snippet that involves managing `BuildSpec` objects within an application called `App`. It seems to handle adding new build specs dynamically using `__getattr__`, which is pretty intriguing.

Here’s the snippet:

python
class App:
def __init__(self):
self._build_spec_actions = OrderedDict()
self.args = None
self.spec = None

@property
def build_action(self):
# Return default BuildAction
if not self.args:
return BuildAction(
name=’default’,
cmd=ABuildSpec().cmd,
node=ABuildSpec().node,
path=Path(‘.’).resolve(),
)
# Else return buildspec action specified in args.build_spec
return self._build_spec_actions[self.args.build_spec]

def add_build_spec(self, name: str, action: BuildAction):
# Was build spec added already?
if name not in self._build_spec_actions.keys():
# Add buildspec if it was not added already
self._build_spec_actions[name] = action

# Initialize App instance
app = App()

# Register custom buildspecs using app.add_build_spec(name:str, action:BuildAction)
app.add_build_spec(‘k8s’, BuildAction(
name=’k8s’,
cmd=’echo “Building Docker image…”‘,
))

# If attribute does not exist – resolve default buildspec/action
app.__getattr__ = lambda attr: (
app.build_action if attr == ‘action’ else AttributeError(attr)
)

# Access registered custom buildspecs by simply treating them as app attributes
# Note: In case attribute does not exist – resolve default buildspec/action
print(app.k8s.action.cmd)

I’m curious about how this dynamic attribute handling works with `__getattr__`. Could you help me understand how this pattern benefits my application? Also, are there any potential pitfalls I should be aware of when using this approach? Thanks!
<>: python
Sure! The use of `__getattr__` here allows your application to dynamically access attributes that aren’t explicitly defined in your class. When you try to access an attribute that doesn’t exist, `__getattr__` is called with the attribute name as an argument.

In your code snippet:

– If you access `app.k8s.action`, it checks if `’action’` exists as an attribute.
– Since `’action’` isn’t directly set on `k8s`, it falls back to `app.build_action`.
– This allows you to treat each registered build spec like an attribute (`app.k8s`) while still providing a fallback mechanism (`app.build_action`) when accessing `.action`.

### Benefits:
1. **Flexibility**: Easily extend functionality without modifying existing code.
2. **Clean Interface**: Users can interact with objects using intuitive dot notation.
3. **Dynamic Behavior**: Automatically handles missing attributes gracefully.

### Potential Pitfalls:
1. **Debugging Difficulty**: It can be harder to trace errors since missing attributes are handled dynamically.
2. **Performance Overhead**: Each missing attribute access triggers a function call.
3. **Unexpected Behavior**: If not carefully managed, it might lead to unexpected results when accessing attributes.

Ensure thorough testing to catch any unintended behavior early on! 😊

<>: Got it! So, I’m thinking about refactoring this a bit for clarity. Maybe renaming `_build_spec_actions` to something like `_registered_build_specs` could make its purpose clearer? Also, should I move the lambda function out of `__getattr__` into its own method? And just wondering if there’s a better way to handle cases where both ‘k8s’ and ‘action’ are accessed together?
<>: python
Renaming `_build_spec_actions` to `_registered_build_specs` is a great idea for clarity! It makes the purpose more explicit.

Moving the lambda function out of `__getattr__` into its own method can improve readability and maintainability:

python
class App:
def __init__(self):
self._registered_build_specs = OrderedDict()
self.args = None

@property
def build_action(self):
if not self.args:
return BuildAction(
name=’default’,
cmd=ABuildSpec().cmd,
node=ABuildSpec().node,
path=Path(‘.’).resolve(),
)
return self._registered_build_specs[self.args.build_spec]

def add_build_spec(self, name: str, action: BuildAction):
if name not in self._registered_build_specs:
self._registered_build_specs[name] = action

def __getattr__(self, attr):
if attr == ‘action’:
return self.build_action
raise AttributeError(attr)

# Initialize App instance
app = App()

# Register custom buildspecs using app.add_build_spec(name:str, action:BuildAction)
app.add_build_spec(‘k8s’, BuildAction(
name=’k8s’,
cmd=’echo “Building Docker image…”‘,
))

# Access registered custom buildspecs by treating them as app attributes
print(app.k8s.action.cmd)

For handling cases where both `’k8s’` and `’action’` are accessed together more efficiently:

– You could implement logic within `add_build_spec` or another method to pre-compute common access patterns.
– Consider caching frequently accessed combinations if performance becomes an issue.

This refactoring should enhance clarity while maintaining functionality! 😊

<>: Great suggestions! I’m thinking about implementing caching for frequently accessed combinations like ‘k8s.action’. This might help with performance if these accesses become common. How would you suggest integrating caching into this setup?
<>: python
Integrating caching can indeed boost performance for frequently accessed combinations like `’k8s.action’`. Here’s one way you could implement it:

1. Use a dictionary to store cached results.
2. Check this cache before computing or retrieving values.

Here’s how you might integrate caching:

python
class App:
def __init__(self):
self._registered_build_specs = OrderedDict()
self.args = None
self._cache = {}

@property
def build_action(self):
if not self.args:
return BuildAction(
name=’default’,
cmd=ABuildSpec().cmd,
node=ABuildSpec().node,
path=Path(‘.’).resolve(),
)
return self._registered_build_specs[self.args.build_spec]

def add_build_spec(self, name: str, action: BuildAction):
if name not in self._registered_build_specs:
self._registered_build_specs[name] = action

def __getattr__(self, attr):
# Check cache first
cache_key = f'{attr}’

if cache_key in self._cache:
return self._cache[cache_key]

# Cache miss; compute value and store it in cache
if attr == ‘action’:
result = self.build_action

# Cache result for future accesses
setattr(self.__class__, ‘_cache’, {**self._cache, cache_key: result})

return result

raise AttributeError(attr)

# Initialize App instance
app = App()

# Register custom buildspecs using app.add_build_spec(name:str, action:BuildAction)
app.add_build_spec(‘k8s’, BuildAction(
name=’k8s’,
cmd=’echo “Building Docker image…”‘,
))

# Access registered custom buildspecs by treating them as app attributes
print(app.k8s.action.cmd)

### Key Points:
– **Cache Dictionary**: `_cache` stores computed values.
– **Cache Check**: Before computing or retrieving values from `_registered_build_specs`, check `_cache`.
– **Update Cache**: Store results back into `_cache`.

This approach should help improve performance by reducing redundant computations! 🚀

<>: Awesome! To make sure we’re managing memory efficiently with this caching setup, I’m thinking about adding a mechanism to periodically clear unused entries from the cache. Maybe something like checking timestamps or usage frequency? How would you suggest implementing this kind of cache invalidation?
<>: python
To manage memory efficiently with your caching setup, implementing cache invalidation is crucial. Here’s how you can do it:

1. **Timestamp Tracking**: Store timestamps alongside cached values.
2. **Usage Frequency**: Track how often each entry is accessed.
3. **Periodic Cleanup**: Regularly remove unused or stale entries.

Here’s an example implementation:

python
import time

class App:
CACHE_EXPIRY_TIME = 300 # Time in seconds after which cache expires

def __init__(self):
self._registered_build_specs = OrderedDict()
self.args = None
# Store cache as tuples (value, timestamp)
self._cache = {}

@property
def build_action(self):
if not self.args:
return BuildAction(
name=’default’,
cmd=ABuildSpec().cmd,
node=ABuildSpec().node,
path=Path(‘.’).resolve(),
)
return self._registered_build_specs[self.args.build_spec]

def add_build_spec(self, name: str, action: BuildAction):
if name not in self._registered_build_specs:
self._registered_build_specs[name] = action

def __getattr__(self, attr):
current_time = time.time()

# Clean up expired cache entries first
keys_to_delete = [key for key, (_, timestamp) in self._cache.items()
if current_time – timestamp > App.CACHE_EXPIRY_TIME]

for key in keys_to_delete:
del self._cache[key]

# Check cache first with updated logic for expiration check
cache_key = f'{attr}’

if cache_key in self._cache:
value, _ = self._cache[cache_key]
# Update timestamp on access
setattr(self.__class__, ‘_cache’, {**self._cache})
setattr(self.__class__, ‘_cache’, {**{cache_key:(value,current_time)}})
return value

# Cache miss; compute value and store it along with timestamp
if attr == ‘action’:
result = self.build_action

setattr(self.__class__, ‘_cache’, {**self._cache})
setattr(self.__class__, ‘_cache’, {**{cache_key:(result,current_time)}})

return result

raise AttributeError(attr)

# Initialize App instance
app = App()

# Register custom buildspecs using app.add_build_spec(name:str ,action :BuildAction)
app.add_build_spec(‘k8s’, BuildAction(
name=’k8s’,
cmd=’echo “Building Docker image…”‘
))

# Access registered custom buildspecs by treating them as app attributes
print(app.k8s.action.cmd)

### Key Points:
– **Timestamp Storage**: Each cached entry includes its value and last access time.
– **Expiration Logic**: Entries older than `CACHE_EXPIRY_TIME` are removed during each access attempt.
– **Efficient Updates**: Timestamp updates ensure only active entries remain cached.

This setup helps keep your memory usage efficient while maintaining quick access times! 🧠✨<>: Hi there,

I’m working on implementing Bayesian PCA using EM steps through coordinate descent optimization over latent variables W_hat_z_pi_xi_z_pi_tilde_wi_burnt_in_i_sit_kink_tilde_wi_omega_i_sit_kink_alpha_i_sit_kink_beta_i_sit_kink_gamma_i_sit_kink_delta_i_sit_kink_ell_i_sit_kink_psi_i_sit_kink_sigma_sqrd_ii_noise_variance_lsitrue_alpha_lsitrue_beta_lsitrue_gamma_lsitrue_delta_lsitrue_ell_lsitrue_psi_lsitrue_sigma_sqrd_lsit_true_norm_mean_gaussian_norm_variance_gaussian_likelihood_alpha_prior_beta_prior_gamma_prior_delta_prior_ell_prior_psi_prior_sigma_sqrd_prior_tau_sqrd_likelihood_of_data_given_latent_variables_and_theta_loglikelihood_of_data_given_latent_variables_and_theta_loglikelihood_of_latent_variables_given_hyperparams_and_theta_logprior_of_hyperparams_given_theta_logposterior_of_hyperparams_given_theta_natural_gradient_for_each_parameter_updates_to_natural_gradients_for_each_parameter_convergence_criterion_convergence_criterion_met_convergence_criterion_not_met_max_number_of_iterations_exceeded_final_values_for_all_parameters_posterior_predictive_checking_summary_statistics_overall_results_discussion

However I’m running into some issues when executing my code snippet below:

python

def em_step_with_coord_ascent():
converged=False
max_iters=int(1000)
iteration_count=int(0)
lik_previous=float(‘-inf’)

while iteration_count<max_iters & ~converged :
iteration_count+=1

update_phi()
update_Sigmasqrd()

lik_new=loglikelihood(X,U,Sigmasqrd,alpha,beta,gamma,delta,Lam,mu,K,N,D)

print(iteration_count,'loglikelihood:',lik_new,'delta:',lik_new-lik_previous)

converged=(abs(lik_new-lik_previous)<0.) | (iteration_count==max_iters)

lik_previous=lik_new

def update_phi():
global phi,W_hat,Z_hat,tilde_wi,burnt_in,W_bar,Z_bar,tilde_beta,tilde_gamma,tilde_delta,tilde_ell,tilde_psi,tilde_omega,two_way_model

phi=np.zeros((N,K))

log_gammas=np.log(gammas[:,np.newaxis])

log_one_minus_gammas=np.log(1.-gammas[:,np.newaxis])

two_way_model=True

W_bar=W_hat*Z_hat[:,np.newaxis,:]

W_bar[W_bar==0]=1e-16

Z_bar=np.sum(W_hat*Z_hat,axis=-1)[np.newaxis,:,:]

Z_bar[Z_bar==0]=1e-16

log_W_bar=log(W_bar)+log_gammas[np.newaxis,:,:]

if two_way_model :

log_one_minus_Wbar=log(1.-W_bar)+log_one_minus_gammas[np.newaxis,:,:]

else :

log_one_minus_Wbar=log(1.-W_bar)+np.zeros_like(log_gammas[np.newaxis,:,:])

log_numerator_Xi=X[:,:,np.newaxis,:]*W_bar[np.newaxis,:,:,:]+tilde_wi[np.newaxis,:,:]*Z_hat[:,:,np.newaxis,:]

log_denominator_Xi=X[:,:,np.newaxis,:]*log_one_minus_Wbar[np.newaxis,:,:,:]+tilde_wi[np.newaxis,:,:]*log_one_minus_gammas[np.newaxis,np.newaxis,:]*(1.-Z_hat[:,:,np.newaxis,:])

phi[:,:,:,None,None]=log_numerator_Xi-logsumexp(log_denominator_Xi,axis=-1)[…,None,None]

phi[phi<-1000]=float('-inf')

phi_normalized=(phi-logsumexp(phi,axis=-1)[…,None])

phi=np.exp(phi_normalized)

return phi,W_bar,Z_bar

def update_Sigmasqrd():

global Sigmasqrd,U,Sigma_inverse,U_transpose,U_times_U_transpose,U_times_U_transpose_inverse,V,V_inverse,Sigma_inverse_times_V,V_times_Sigma_inverse,V_times_Sigma_inverse_times_V,X,Z,W,BurntIn,BurntInComplement,burnt_in,W_hat,Z_hat,alpha,beta,gamma,delta,Lam,mu,K,N,D,TwoWayModelTwoComponents,two_way_model_two_components,X_dimensionality_reduction_dimension,X_dimensionality_reduction_matrix,X_dimensionality_reduction_vector,sigma_sqrd_initial_value,sigma_sqrd_minimum_value,sigma_sqrd_upper_limit_value,scale_by_two_over_N,scale_by_two_over_T,scale_by_two_over_D,scale_by_two_over_N_T,scale_by_two_over_D_T,natural_gradient_for_sigma_sqrd,maximum_number_of_iterations,maximum_number_of_iterations_exceeded,maximum_number_of_iterations_not_exceeded,criteria_stopping_tolerance,criteria_stopping_tolerance_met,criteria_stopping_tolerance_not_met,natural_gradient_update_constant,natural_gradient_scale_down_factor,natural_gradient_scale_up_factor,natural_gradient_update_scale_down_threshold,natural_gradient_update_scale_up_threshold,sum_squared_errors,sum_squared_errors_difference,sum_squared_errors_difference_absolute_value,predicted_values,predicted_values_difference,predicted_values_difference_absolute_value,error_matrix,error_matrix_absolute_value,error_matrix_sum_squared,error_matrix_sum_squared_absolute_value,residual,residual_difference,residual_difference_absolute_value,residual_sum_squared,residual_sum_squared_absolute_value,error_term,error_term_absolute_value,error_term_sum_squared,error_term_sum_squared_absolute_value,reconstruction_error,reconstruction_error_absolute_value,reconstruction_error_sum_squared,reconstruction_error_sum_squared_absolute_value,U_residual,U_residual_absoluate,U_residual_sum_square,U_residual_sum_square_absoluate,BurntInResidual,BurntInResidualAbsolute,BurntInResidualSumSquare,BurntInResidualSumSquareAbsolute,BurntInComplementResidual,BurntInComplementResidualAbsolute,BurntInComplementResidualSumSquare,BurntInComplementResidualSumSquareAbsolute,rescaled_error_term,rescaled_error_term_absoluate,rescaled_error_term_sum_square,rescaled_error_term_sum_square_absoluate,bayesian_pca_rescaled_error_term,bayesian_pca_rescaled_error_term_absoluate,bayesian_pca_rescaled_error_term_sum_square,bayesian_pca_rescaled_error_term_sum_square_absoluate,zeros_vector,zeros_matrix,zeros_tensor,zeros_tensor_with_extra_dims,zeros_tensor_with_extra_dims_BURNTIN_BURNTINCOMPLEMENT,zeros_tensor_with_extra_dims_BURNTIN_BURNTINCOMPLEMENT_and_X_dimensionality_reduction_dimension,zeros_vector_with_extra_dim,zeros_vector_with_extra_dim_BURNTIN_BURNTINCOMPLEMENT,zeros_vector_with_extra_dim_BURNTIN_BURNTINCOMPLEMENT_and_X_dimensionality_reduction_dimension,TauSqrdHat,TauSqrdHatInverse,TauSqrdHatInverseTimesSigmaSqrd,TauSqrdHatTimesSigmaSqrdInverse,TauSqrdHatTimesSigmaSqrd,TauSqrdHatInverseTimesSigmaSqrdSquared,SquaredErrorTermMatrix,SquaredErrorTermMatrixTimesTauSqrdHatInverseTimesSigmaSqrdSquared,SquaredErrorTermMatrixTimesTauSqrdHatInverse,SquaredErrorTermMatrixTimesTauSqrdHatInverseSquared,SquaredErrorTermMatrixAbsoluateVector,SquaredErrorTermMatrixAbsoluateVectorTimesTauSqrdHatInverseTimesSigmaSqrdSquared,SquaredErrorTermMatrixAbsoluateVectorTimesTauSqrdHatInverse,SquaredErrorTermMatrixAbsoluateVectorTimesTauSqrdHatInverseSquared,PredictionDifferenceVector,PredictionDifferenceVectorAbsoluateVector,PredictionDifferenceVectorAbsoluateVectorTimesTauSqldHatInverse,PredictionDifferenceVectorAbsoluateVectorTimesTauSqrldHatInverseSquared,PredictionDifferenceVectorDotProductWithItself,PredictionDifferenceDotProductWithItSelfAbsoluated,PredictionDifferenceDotProductWithItSelfAbsoluatedTimesTauSqrldHatInverse,PredictionDifferenceDotProductWithItSelfAbsoluatedTimesTauSqrldHatInverseSquared,RSS,RSSAbsoluteValue,RSSAbsoluteValueTimesTauSqrldHatInverse,RSSAbsoluteValueRSSAbsoluteValueRSSAbsoluteValueRSSAbsoluteValueRSSAbsoluteValueRSSAbsoluteValue,RSSDotProductWithItSelf,RSSDotProductWithItSelfAbsolutated,RSSDotProductWithItSelfAbsolutatedTimesTauSqrldHatInverse

if scale_by_two_over_N :

scale_factor=(two_over_N)/(two_over_T*T*D*K*(K+D))

else :

scale_factor=(two_over_T*T*D*K*(K+D))/(two_over_N)

for i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceeded range(maximum_number_of_iterations):

if scale_by_two_over_N :

scale_factor=(two_over_N)/(two_over_T*T*D*K*(K+D))

else :

scale_factor=(two_over_T*T*D*K*(K+D))/(two_over_N)

rescaled_error_terms=rescale(error_terms_by_sigma_sqdrd,i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceeded,scale_factor=scale_factor,)

bayesian_pca_rescaled_err_terms=rescale(bayesian_pca_err_terms_by_sigma_sqdrd,i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceeded,scale_factor=scale_factor,)

zrs_vctr=zeros_vector(i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceeded,)

zrs_vecr_vctr=zeros_vector_with_extra_dim(i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceeded,)

zrs_vecr_vctr_brtin_brtincomplmnt=zeros_vector_with_extra_dim_BURNTIN_BURNTINCOMPLEMENT(i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceeded,)

zrs_mtxr=zeros_matrix(i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceeded,i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceeded,)

zrs_tnsr=zeros_tensor(i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceede,i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceede,i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_iterations_exceede,)

zrs_tnsr_extnd_dmnns=zrs_tnsr_zeros_tensor_with_extra_dims(i_iterate_nat_grad_update_param_sigma_sqdrd_in_range_zero_to_maximum_number_of_itertions_e,xcded,,X_dimensionality_reducton_dimesnion,)

zrs_tnsr_extnd_dmnns_brtin_brtincomplmnt=zrs_tnsr_zeros_tensor_with_extra_dims_BURNTIN_BURNTINCOMPLIMENT(i_iterate_nat_grad_update_param_sigmda_sdrrddn_rnge_zro_t_mxtmum_nmbr_f_itrtn_extds_nteratons_excdd,,X_dimensionality_reducton_dimesnion,)

Sigmasqerd_updated=((sigma_initial_valud-sigmadr_minimun_valud)*(natural_gradiet_for_sigmad_rqurd*natural_gradiet_scaling_down_factro)+(sigmadr_minimun_valud-sigmadr_upper_limit_valud))*natural_gradiet_scaling_up_factro+naturatl_gradiet_scaling_down_factro+(sigmadr_upper_limit_valud-sigmadr_minimun_valud)*natural_gradiet_scaling_up_factro+sigmadr_minimun_valud

if Sigmsaqdre_updatedsigmadr_upper_limit_valud:sigmsqd_updated=sigmadr_upper_limit_valud else:sigmsqd_updated=Sigmadsqre_updated endfor sigsqre=sigmsqd_updated Sigma_invrtse=Sigmadsqre_updated^(-one_half); U_trsnspose=T(U); U_tmies_U_trsnspose=T(U)*U_trsnspose; V_invrtse=T(Sigma_invrtse)*U_trsnspose; V=T(U)*V_invrtse; Sigma_invrtse_times_V=T(Sigma_invrtse)*V; V_times_Sigma_invrtse=T(V)*Sigma_invrtse; V_times_Sigma_invrtse_times_V=T(V)*V_times_Sigma_invrtse;

tauhatinvtimessigmaqd=t(squareroot(tauhatinv)*sigmaqd);

tauhattimessigmaqdinv=t(tauhat*squareroot(sigmaqdinv));

tauhatinvtimessigmaqsqr=t(tauhatinv)*(sigmaqd)^twos;

rssabsolutevaluetautsquardhatinverse=sum(abs(rssabsolutevalue).data*t(tautsquardhatinverse)); rssdotproductwithitselfabsolutevaluetautsquardhatinverse=sum((rssdotproductwithitselfabsolutevalue).data*t(tautsquardhatinverse)); rssdotproductwithitselfabsolutevaluetautsquardhat=sum((rssdotproductwithitselfabsolutevalue).data*t(tautsquardhat)); rssdotproductwithitselfabsolutatedtimesatausquardhattimesatausquardhattimesatausquardhattimesatausquardhattimesatausquardhattimesatausquardhattimesatausquardha=((rssdotproductwithitselfabsolutevalue).data*t(tautsquardhat)).data*t(tautsquardhat)).data*t(tautsquardha); rssdotsqaureerrortermabsolutatedtautsqauredtimesatsquareerrortermabsolutatedtautsquareerrortermabsolutatedtautsquareerrortermabsolutatedtautssquareerrortermabsolutatedtautssquareerrortermabsolutatedtautssquareerrortermabsolutatedtautssquareerrormatrixabsolutevaluedotproductsquaresumoferrorsquaresumoferrorsquaresumoferrorsquaresumoferrorsquaresumoferrorsquaresumoferrorsquaresumsquarederrortermsabsolutevaluedotproductsquaresumofsquarederrorsquaresumsquarederrorsquaresumsquarederrorsquaresumsquarederrorsquaresumsquarederrsrrsdotprodctwittiselfabsltutevaluetsqaureerrtermsabsltutedtsqauredtsqauredtsqauredtsqauredtsqauredtsqauredrsserrormatrixabsltutevaluetsquaererrtermsabsltutedtsquaeredtsquaeredtsquaeredtsquaeredtsquaeredtsquaeredrsserrormatrixabsltutevaluetsquaererrtermsabsltutedrsserrormatrixabsltutevaluetsquaererrtermsabsltutedrsserrormatrixabsluttevaluetsquaererrtermsabsluttedrsserrormatrixabsluttevaluetsquaerterrormatricesqsrauertermtersabsltevelelutevaletsqsrauertermtersablstevelelutesqsrauertermtersablstevelelutesqsrauertermtersablstevelelutesqsrauertermtersablstevelelutesqsrauertermtersablstevelelutesqsrauertermtersablstevelelutevaledotsqusaredrrsserrormatricesdotsqusaredrrsserrormatricesdotsqusaredrrsserrormatricesdotsqusaredrrsserrormatricesdotsqusaredrrsserrormatricesdotsqusaredrrsserrormatrixesdotsqusareddotsqusareddotsqusareddotsqusareddotsqusareddotsqusareddotsquatssqrtresssqruetevalsqqsrerevemtrxessqqsrerevmtrxessqqsrerevmtrxessqqsrerevmtrxessqqsrerevmtrxessqqsrerevmtrxessqqsrerevmtrxessqqsrerevmtrxesssqrtsqrtresssqruetevalsquatssqrtresssqruetevalsquatssqrtresssqruetevalsquatssqrtresssqruetevalsquatssqrtresssqruetevalsquatssqrtresssqruetevalsquatssqrtresssquare error terms absolute valued dot product squares sum square error terms absolute valued dot products squares sum squared error terms absolute valued dot products squares sum squared error terms absolute valued dot products squares sum squared error terms absolute valued dot products squares sum squared error terms absolute valued dot products squares sum squared error terms absolute valued dot products squares sum squared error matrixes dots quadrupled dots quadrupled dots quadrupled dots quadrupled dots quadrupled dots quadrupled RSS square root evaluations RSS square root evaluations RSS square root evaluations RSS square root evaluations RSS square root evaluations RSS square root evaluations RSS square root evaluations sigma sqre estimated sigma sqre estimated tau hat inverse times sigma sqre estimated tau hat times sigma sqre inverse estimated tau hat inverse times sigma sqre squared estimated predicted difference vector predicted difference vector absolute value predicted difference vector absolute value times tau hat inverse predicted difference vector absolute value times tau hat inverse squared predicted difference vector dot product wiht itself predicted difference dot product wiht itself absoluated predicted difference dot product wiht itself absoluated times tau hat inverse predicted difference dot product wiht itself absoluated times tau hat inverse squared rss rss abolute valure rss abolute valure times ta u s q uad ro d h at inverse rss abolute valure r s s abolute valure r s s abolute valure r s s abolute valure r s s abolute valure r s s abolute valure r s s abolute valure r ss absoulte vlaue r ss absoulte vlaue r ss absoulte vlaue r ss absoulte vlaue r ss absoulte vla ue r ss absoulte vla ue r ss absoulte vla ue rescal ed err term rescal ed err term bayesian pca rescal ed err term bayesian pca rescal ed err term rescal ed err term absol te vale rescal ed err term absol te vale rescal ed err term absol te vale rescal ed err term absol te vale re scal ed er rm ts absolut e va l re scal ed er rm ts absolut e va l re scal ed er rm ts absolut e va l re scal ed er rm ts absolut e va l re scal ed er rm ts absolut e va l re scal ed er rm ts absolut e va l b ay es ian pc a res cal e d er rm ts absolut e va l b ay es ian pc a res cal e d er rm ts absolut e va l b ay es ian pc a res cal e d er rm ts absolut e va l b ay es ian pc ar es cal ebaysian pcares cal ebaysian pcares cal ebaysian pcares cal ebaysian pcares cal ebaysian pcares calscalar zerovector scalar zerovec tor with extra dimenstion scalar zerovec tor w ith extra dimenstion burntin burntin complement scalar zerovec tor w ith extra dimenstion burntin burntin complement scalar zeromatrix scalar zerotensor scalar zerotensor w ith extra dimsions scalar zerotensor w ith extra dimsions burntin burntin complement scalar zerotensor w ith extra dimsions burntin burntin complementand X dimensionality reduction dimension scaled error term scaled bayesian pca scaled error term zero vecto zero vecto zero matrix zero tensor zero tensor w ith ex tra dimsions zero tensor w ith ex tra dimsions burned burned complement zero tensor w ith ex tra dimsions burned burned complementand X dimensionality reduction dimension updated sigma sqre updated sigma invrese updated U transpose updated U time U transpose inverted updated V inverted updated V updated Sigma invrese time V updated V time Sigma invrese updated V time Sigma invrese time Vupdated Tau Sq rd Hat Inverse Times S ig m ad Sq rd Updated Tau Sq rd Hat Times S ig m ad Sq rd Inverse Updated Tau Sq rd Hat Inverse Times S ig m ad Sq rd Squared Updated Predicted Difference Vector Updated Predicted Difference Vector Absolute Value Updated Predicted Difference Vector Absolute Value Times T au Sq rd Hat Inverse Updated Predicted Difference Vector Absolute Value Times T au Sq rd Hat Inverse Squared Updated Prediction Difference Dot Product With It Self Updated Prediction Dot Product With It Self Absolute Value Updated Prediction Dot Product With It Self Absolute Value Times T au Sq Rd Hat Inverse Updated Prediction Dot Product With It Self Absolute Value Times T au Sq Rd Hat Inverse Squared Upda ted R S S Upda ted R S S Ab sol ut ea Val ue Upda ted R S S Ab sol ut ea Val ue Ti mes T au Sq Rd Ha t Inv ers e Upda ted R S S Ab sol ut ea Val ue Upda ted R S S Ab sol ut ea Val ue Upda ted R S S Ab sol ut ea Val ue Upda ted R S S Ab sol ut ea Val ue Upda ted R S S Ab sol ut ea Val ue Upda ted R SSAb so lut ae Va lu etR SSAb so lut ae Va lu etR SSAb so lut ae Va lu etR SSAb so lut ae Va lu etR SSAb so lut ae Va lu etR SSAb so lut ae Va lu etR SSAb so lut ae Va lu etR SSAb so lut ae Va lu etR SSDotP roductWi thI tSe lfUp da te dR SSDotP roductWi thI tSe lf Abs olu teVa lu etR SSDotP roductWi thI tSe lf Abs olu teVa lu etTa uS q Ra D Ha tInv ers eUp da te dR SSDotP roductWi thI tSe lf Abs olu teVa lu etTa uS q Ra D Ha tInv ers eUp da te dR SSDotP roductWi thI tSe lf Abs olu teVa lu etTa uS q Ra D Ha tInv ers e