🔴 High pressing tactics disrupt opponent strategies effectively.
#Cons: ❌❌❌❌❌ Occasional lapses in concentration lead to costly errors.
Struggles with maintaining possession under intense pressure.
None:
self.cos_sim_default_eps_zero_eps_zero_grads_none_shape_one_dim_x_y_same_size_y_first_dim_batch_size_y_second_dim_features_x_same_as_y_but_last_dim_is_labels_shape_two_dim_x_y_same_size_y_first_dim_batch_size_y_second_dim_features_x_same_as_y_but_last_dim_is_labels_shape_three_dim_x_y_same_size_y_first_dim_batch_size_y_second_dim_num_channels_or_height_or_width_or_depth_x_same_as_y_but_last_dim_is_labels_shape_one_and_two_and_three_plus_n_dims_x_same_as_y_but_last_dim_is_labels_shape_one_and_two_and_three_plus_n_dims_labelled_pairwise_loss_all_pairs_with_equal_number_of_classes_in_each_batch_labelled_pairwise_loss_all_pairs_with_different_number_of_classes_in_each_batch_unlabelled_pairwise_loss_all_pairs_with_equal_number_of_classes_in_each_batch_unlabelled_pairwise_loss_all_pairs_with_different_number_of_classes_in_each_batch_unlabelled_contrastive_loss_all_pairs_with_equal_number_of_classes_in_each_batch_unlabelled_contrastive_loss_all_pairs_with_different_number_of_classes_in_each_batch_unlabelled_contrastive_loss_with_multiple_negative_samples_per_positive_sample_all_pairs_with_equal_number_of_classes_in_each_batch_unlabelled_contrastive_loss_with_multiple_negative_samples_per_positive_sample_all_pairs_with_different_number_of_classes_in_each_batch_unlabelled_triplet_semihard_loss_all_pairs_with_equal_number_of_classes_in_each_batch_unlabelled_triplet_semihard_loss_all_pairs_with_different_number_of_classes_in_each_batch_unlabelled_triplet_ehard_n_hard_negatives_one_hundred_percent_sampling_rate_for_the_hardest_negative_sample_only_unlabelled_triplet_ehard_n_hard_negatives_five_percent_sampling_rate_for_the_hardest_negative_sample_only_labeled_contrastive_loss_labeled_pairwise_margin_ranking_loss_labeled_soft_margin_ranking_loss_labeled_soft_triple_margin_ranking_loss_labeled_multi_class_hinge_embedding_loss_softmax_classification_cross_entropy_softmax_classification_cross_entropy_without_temperature_softmax_classification_cross_entropy_without_temperature_weighted_by_class_frequency_inverse_sqrt_softmax_classification_cross_entropy_without_temperature_weighted_by_class_frequency_inverse_sqrt_softmax_classification_cross_entropy_without_temperature_weighted_by_class_frequency_log_uniform_softmax_classification_cross_entropy_weighted_by_class_frequency_log_uniform_softmax_classification_cross_entropy_weighted_by_class_frequency_log_uniform_sigmoid_binary_cross_entropy_sigmoid_binary_cross_entropy_weighted_by_class_frequency_log_uniform_sigmoid_binary_cross_entropy_weighted_by_class_frequency_log_uniform_multilabel_soft_margin_multilabel_soft_margin_weighted_by_class_frequency_log_uniform_multilabel_soft_margin_weighted_by_class_frequency_log_uniform_multiclass_hinge_embedding_multiclass_hinge_embedding_weighted_by_class_frequency_log_uniform_multiclass_hinge_embedding_weighted_by_class_frequency_log_uniform_centroid_losses_centroid_losses_centroid_losses_when_using_cosine_similarity_centroid_losses_when_using_cosine_similarity_when_providing_custom_centroids_criterion_for_autoencoder_reconstruction_error_mean_squared_error_mean_squared_error_when_providing_custom_reconstruction_targets_criterion_for_autoencoder_reconstruction_error_mean_absolute_error_mean_absolute_error_when_providing_custom_reconstruction_targets_criterion_for_autoencoder_reconstruction_error_mean_absolute_percentage_error_mean_absolute_percentage_error_when_providing_custom_reconstruction_targets_criterion_for_autoencoder_reconstruction_error_root_mean_squared_error_root_mean_squared_error_when_providing_custom_reconstruction_targets_criterion_for_autoencoder_reconstruction_error_root_mean_squared_logarithmic_error_root_mean_squared_logarithmic_error_when_providing_custom_reconstruction_targets_criterion_for_autoencoder_reconstruction_error_symmetric_mse_symmetric_mse_when_providing_custom_reconstruction_targets_symmetric_mae_symmetric_mae_when_providing_custom_reconstruction_targets_symmetric_map_symmetric_map_when_providing_custom_reconstruction_targets_symmetric_rmse_symmetric_rmse_when_providing_custom_reconstruction_targets_symmetric_rmsle_symmetric_rmsle_when_providing_custom_reconstruction_targets_bce_bce_logits_bce_logits_weights_mask_bce_logits_weights_mask_float_tensor_float_tensor_float_tensor_float_tensor_int_tensor_int_tensor_int_tensor_int_tensor_int_tensor_int_tensor_int_tensor_float_tensor_float_tensor_float_tensor_float_tensor_bool_tensor_bool_tensor_bool_tensor_bool_tensor_bool__tensor__tensor__tensor__tensor__tensor__tensor__tensor__tensor__tensor__tensor__tensor___eps_cos_sim_eps_default_eps_zero_grads_none_grads_none_shape_one_labelled_pairwise_labelled_pairwise_labelled_contrastive_labeled_contrastive_labeled_triplet_semihard_labeled_triplet_semihard_unlabelled_triplet_ehard_n_hard_negatives_five_percent_sampling_rate_unlabelled_triplet_ehard_n_hard_negatives_five_percent_sampling_rate_softmax_classification_cross_entropy_softmax_classification_cross_entropy_without_temperature_softmax_classification_cross_entropy_without_temperature_weighted_by_class_frequency_inverse_sqrt_softmax_classification_cross_entropy_without_temperature_weighted_by_class_frequency_inverse_sqrt_softmax_classification_cross_entropy_without_temperature_weighted_by_class_frequency_log_uniform_softmax_classification_cross_entropy_weighted_by_class_frequency_log_uniform_sigmoid_binary_crossentropy_sigmoid_binary_crossentropy_weights_mask_sigmoid_binary_crossentropy_weights_mask_multilabel_softmargin_multilabel_softmargin_weights_mask_multilabel_softmargin_weights_mask_multiclass_hinge_embedding_multiclass_hinge_embedding_weights_mask_multiclass_hinge_embedding_weights_mask_centroid_losses_centroid_losses_centroid_losses_cosine_similarity_cosine_similarity_centroid_losses_cosine_similarity_centroid_losses_cosine_similarity_ae_rec_err_mse_ae_rec_err_mse_ae_rec_err_mse_ae_rec_err_target_ae_rec_err_target_ae_rec_err_mae_ae_rec_err_mae_ae_rec_err_target_ae_rec_err_target_ae_rec_err_map_ae_rec_err_map_ae_rec_err_target_ae_rec_err_target_ae_rec_err_rmse_ae_rec_err_rmse_ae_rec_err_target_ae_rec_err_target_ae_rec_err_rmsle_ae_rec_err_rmsle_ae_rec_err_target_ae_rec_err_target_sympm_sympm_sympm_sympm_sympm_sympm_sympm_sympm_sympm_sympm_smclsxent_smclsxent_smclsxent_smclsxent_smclsxent_smclsxent_smclsxent_smclsxent_wt_inv_sqrt_wt_inv_sqrt_wt_ln_wt_ln_wt_ln_wt_ln_sigmbce_sigmbce_sigmbce_sigmbce_sigmbce_sigmbce_sigmbce_sigmbce_wtsmask_sigmbce_wtsmask_sigmbce_wtsmask_mlsl_mlsl_mlsl_mlsl_mlsl_mlsl_mlsl_mlsl_mcml_mcml_mcml_mcml_mcml_mcml_mcml_mcml_ctrlss_ctrlss_ctrlss_ctrlss_ctrlss_ctrlss_ctrlss_ctrlss_cs_cs_cs_cs_cs_cs_cs_cs_cs_ccsm_ccsm_ccsm_ccsm_ccsm_ccsm_ccsm_ccsm_arccos_arccos_arccos_arccos_arccos_arccos_arccos_arccos_acs_acs_acs_acs_acs_acs_acs_acs_aaee_aaee_aaee_aaee_aaee_aaee_aaee_aaee_areta_areta_areta_areta_areta_areta_areta_areta_amre_amre_amre_amre_amre_amre_amre_amre_atpe_atpe_atpe_atpe_atpe_atpe_atpe_atpe_saepmaesaesmaesaesmaesaesmaesaesmaesaesaepmaesaesmaesaesmaesaesmaesaesamare_maere_maere_maere_maere_maere_maere_maere_maere_armrm_armrm_armrm_armrm_armrm_armrm_armrm_armrm_arrlm_arrlm_arrlm_arrlm_arrlm_arrlm_arrlm_arrlm_apmm_apmm_apmm_apmm_apmm_apmm_apmm_apmm_slxsxl_slxsxl_slxsxl_slxsxl_slxsxl_slxsxl_slxsxl_slxsxl_ilnsln_ilnsln_ilnsln_ilnsln_ilnsln_ilnsln_ilnsln_ilnsln_iwlswl_iwlswl_iwlswl_iwlswl_iwlswl_iwlswl_iwlswl_ibmsbm_ibmsbm_ibmsbm_ibmsbm_ibmsbm_ibmsbm_ibmsbm_iccsic_iccsic_iccsic_iccsic_iccsic_iccsic_iccsic_icbsib_icbsib_icbsib_icbsib_icbsib_icbsib_icbsib_imtsim_imtsim_imtsim_imtsim_imtsim_imtsim_imtsim_ixgixg_ixgixg_ixgixg_ixgixg_ixgixg_ixgixg_ixbixb_ixbixb_ixbixb_ixbixb_ixbixb_ixbixb_ipipip_ipipip_ipipip_ipipip_ipipip_ipipip_ipipip_irrirr_irrirr_irrirr_irrirr_irrirr_irrirr_irrirr_itetit_itetit_itetit_itetit_itetit_itetit_itetit_ioeosio_eosoio_eosoio_eosoio_eosoio_eosoio_eosoio_ieoeie_ieoeie_ieoeie_ieoeie_ieoeie_ieoeie_ioeosio_eososiosiosiosiosiosiosiosiosiosiosioso_so_so_so_so_so_so_so_so_so_se_se_se_se_se_se_se_se_si_si_si_si_si_si_si_si_sr_sr_sr_sr_sr_sr_sr_sr_sq_sq_sq_sq_sq_sq_sq_sq_sb_sb_sb_sb_sb_sb_sb_sb_sg_sg_sg_sg_sg_sg_sg_sg_sk_sk_sk_sk_sk_sk_sk_sk_sn_sn_sn_sn_sn_sn_sn_sn_sp_sp_sp_sp_sp_sp_sp_sp_st_st_st_st_st_st_st_su_su_su_su_su_su_su_su_sw_sw_sw_sw_sw_sw_sw_sw_ta_ta_ta_ta_ta_ta_ta_ta_tb_tb_tb_tb_tb_tb_tb_tb_tc_tc_tc_tc_tc_tc_tc_tc_td_td_td_td_td_td_td_td_te_te_te_te_te_te_te_te_tf_tf_tf_tf_tf_tf_tf_tf_tg_tg_tg_tg_tg_tg_tg_tg_tg_th_th_th_th_th_th_th_th_ti_ti_ti_ti_ti_ti_ti_ti_tj_tj_tj_tj_tj_tj_tj_tj_tl_tl_tl_tl_tl_tl_tl_tl_tm_tm_tm_tm_tm_tm_tm_tm_tp_tp_tp_tp_tp_tp_tp_tp_tr_tr_tr_tr_tr_tr_tr_tr_ts_ts_ts_ts_ts_ts_ts_ts_tt_tt_tt_tt_tt_tt_tt_tt_tv_tv_tv_tv_tv_tv_tv_tw_tw_tw_tw_tw_tw_tw_tw_ty_ty_ty_ty_ty_ty_ty_ty_uu_uu_uu_uu_uu_uu_uu_uu_vv_vv_vv_vv_vv_vv_vv_vv_xx_xx_xx_xx_xx_xx_xx_xx_xy_xy_xy_xy_xy_xy_xy_xy_xz_xz_xz_xz_xz_xz_xz_zz_zz_zz_zz_zz_zz_zz_zz)
***** Tag Data *****
ID: 4
description: Setup method initializing numerous variables used throughout different
tests related to cosine similarity calculations.
start line: 11
end line: 11
dependencies:
– type: Class
name: TestDistances
start line: 13
end line: 13
context description: This setup method initializes many variables used across various
tests within `TestDistances`. It ensures consistency across tests but can be quite
complex due to numerous initializations.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 3
interesting for students: 4
self contained: N
************
## Challenging aspects
### Challenging aspects in above code:
The provided snippet imports several distance metrics from `pytorch_metric_learning.distances`. Here are some challenging aspects:
* **Understanding Different Distance Metrics**: Each imported distance metric (`CosineSimilarity`, `LpDistance`, `MinkowskiDistance`, `NormDistance`) operates differently based on mathematical principles which require deep understanding.
* **Parameter Sensitivity**: Each metric might have specific parameters that influence its behavior significantly; handling these parameters correctly requires careful attention.
* **Integration Complexity**: Integrating these metrics into a consistent testing framework where each metric is tested uniformly yet accurately can be challenging.
* **Edge Cases Handling**: Ensuring robustness against edge cases such as zero vectors or very large/small values can be non-trivial.
### Extension:
Here are some ways these aspects can be extended:
* **Dynamic Metric Selection**: Allow dynamic selection of metrics during runtime based on certain conditions or configurations.
* **Custom Distance Metrics**: Enable users to define custom distance metrics which can be plugged into this framework seamlessly.
* **Parameter Tunability**: Implement functionality where parameters for each metric can be tuned dynamically based on input data characteristics.
## Exercise
### Problem Statement:
You are tasked with extending a testing framework for various distance metrics used within machine learning models implemented using PyTorch Metric Learning library.
**Requirements**:
#### Part A:
Expand [SNIPPET] by creating an advanced testing suite within `TestDistances` class which includes:
* Dynamic selection mechanism allowing any combination of `CosineSimilarity`, `LpDistance`, `MinkowskiDistance`, `NormDistance` during runtime.
* Ability to add custom-defined distance metrics into this suite without altering existing code structure significantly.
#### Part B:
Implement additional functionality that allows tuning parameters dynamically based on input data characteristics such as dimensionality or value range.
#### Constraints:
– Ensure backward compatibility so existing tests do not break upon integration of new functionalities.
– Handle edge cases such as zero vectors gracefully without causing exceptions.
## Solution
### Part A Solution:
python
import unittest
from pytorch_metric_learning.distances import CosineSimilarity, LpDistance, MinkowskiDistance, NormDistance
class TestDistances(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.distance_metrics_dict = {
‘cosine’: CosineSimilarity(),
‘lp’: LpDistance(),
‘minkowski’: Minkowski Distance(),
‘norm’: Norm Distance()
}
@classmethod
def register_metric(cls, name:str, metric):
cls.distance_metrics_dict[name] = metric
# Example usage:
# TestDistances.register_metric(‘custom’, CustomMetric())
### Part B Solution:
python
class DynamicParameterTuner:
@staticmethod
def tune_parameters(metric_name:str):
if metric_name not in TestDistances.distance_metrics_dict.keys():
raise ValueError(f”Metric {metric_name} not registered”)
metric_instance = TestDistances.distance_metrics_dict.get(metric_name)
if isinstance(metric_instance, CosineSimilarity):
return {‘epsilon’: max(0.001 * len(metric_instance.input_data), min_value)}
elif isinstance(metric_instance,(Lp Distance,M ink ows k i Dist ance)):
return {‘ p ‘: min(max(len(metric_instance.input_data) //10000 + min_value)}
# Usage example inside test cases within TestDistances class:
def test_dynamic_param_selection(self):
for name,metric_instacein self.distance_metrics_dict.items():
params=tune_parameters(name)
self.assertGreaterEqual(params[‘epsilon’],min_value) if name==’cosine’ else self.assertGreaterEqual(params[‘ p’],min_value)
## Follow-up exercise
### Problem Statement:
Building upon your implementation from above exercises,
#### Part C:
Enhance your solution to support parallel execution where multiple metrics are tested simultaneously using multi-threading/multiprocessing techniques ensuring thread-safety.
#### Part D:
Introduce logging mechanisms that record detailed logs about which metrics were selected dynamically during runtime along with their parameter settings.
## Solution:
### Part C Solution:
python
import threading
class ThreadSafeTestRunner(Thread):
def __init__(self,test_case_name,params_list):
super().__init__()
self.test_case_name=test_case_name
self.params_list=params_list
def run(self):
test_method=getattr(TestDistances,’test_’+test_case_name)
for params in self.params_list:test_method(**params)
# Example usage inside test case method within TestDistances class:
def test_parallel_execution(self):
threads=[]
for name,param_setin enumerate(params_sets):
thread_runner=ThreadSafeTestRunner(f’dynamic_param_selection_{name}’,param_set)
thread_runner.start()
threads.append(thread_runner)
for threadin threads: thread.join()
### Part D Solution:
python
import logging
logging.basicConfig(level=logging.INFO,filename=’metrics_test.log’)
class LoggingTuner(DynamicParameterTuner):
@staticmethod
def tune_parameters(metric_name:str,**kwargs):
params=tune_parameters.__func__(DynamicParameterTuner,metric_name,**kwargs)
logging.info(f’Metric:{metric_name}, Parameters:{params}’)return params
# Usage example inside test case methods within TestDistances class would remain similar but will now log details automatically.
function GetEntity(eid)
{
return Entities[eid];
}
function GetEntityByPos(x,y,z)
{
for(var i=Entities.length;i–;)
{
var e;
if((
e=(Entities[i])&&
(
e.x==x&&
e.y==y&&
e.z==z
)
))
{
return e;
}
}
return null;
}
function Entity()
{
this.eid=-999;
this.x=-999;
this.y=-999;
this.z=-999;
this.type=null;
this.speedX=null;
this.speedY=null;
this.speedZ=null;
this.hp=null;
this.alive=true;
this.buffList=[];
}
function EntityPlayer(eid,x,y,z,hair,clothes,color,bodycolor,speedX,speedY,speedZ,hunger,maxhp,hungermax)
{
Entity.call(this);
this.eid=eid;
this.x=x||null;this.y=y||null;this.z=z||null;
this.hair=headhair=clothes=headclothes=color=headcolor=this.hair=clothes=this.clothes=color=this.color=this.bodycolor=headbodycolor=this.bodycolor=null;
if(hair!=null)this.hair=headhair=hair;if(clothes!=null)this.clothes=headclothes=clothes;if(color!=null)this.color=headcolor=color;if(bodycolor!=null)this.bodycolor=headbodycolor=bodycolor;
if(speedX!=null)this.speedX=speedX;if(speedY!=null)this.speedY=speedY;if(speedZ!=null)this.speedZ=speedZ;
if(hunger!=null)this.hunger=hunger;if(maxhp!=null)this.maxhp=maxhp;if(hungermax!=null)this.hungermax=hungermax;
}
EntityPlayer.prototype=new Entity();
EntityPlayer.prototype.constructor=function(){return EntityPlayer;};
function EntityItem(eid,x,y,z,type,count)
{
Entity.call(this);
this.eid=eid;
this.x=x||null;this.y=y||null;this.z=z||null;
if(type>=ItemType.length)return;//Invalid item type
if(count==undefined || countthis.maxStack())count=this.maxStack();//If count > maxStack set it equal maxStack
var itemType={type:type,count:this.count=count};
var itemMaxStack=itemType.maxStack();
if(itemType.count>this.maxStack())itemType.count=itemMaxStack;//If count > maxStack set it equal maxStack
itemType.stackSize=itemMaxStack;//Set stack size
itemType.name=itemType.name?itemType.name:”Unknown Item”;//Set default item name
itemType.value=itemType.value?itemType.value:”Unknown Item”;//Set default item value
itemType.icon=itemType.icon?itemType.icon:”Unknown Item”;//Set default item icon
itemType.solid=itemSolid=false;//Items are not solid by default
itemType.burnTime=-999;//Default burn time
itemType.canBeBurnedByFire=false;//Items cannot burn unless specified
itemType.burnTimePerTick=-999;//Default burn time per tick
itemType.isFood=false;//Items cannot be eaten unless specified
itemType.foodHealAmount=-999;//Default heal amount when eaten
itemType.foodSaturationAmount=-999;//Default saturation amount when eaten
itemType.foodHungerRestoreAmount=-999;//Default hunger restore amount when eaten
Item[itemId]=itemItem={type:itemId,count:this.count=count};//Create item object
Item[itemId].maxStack=itemMaxStack;//Set stack size
Item[itemId].name=itemName=itemName?itemName:”Unknown Item”;//Set default item name
Item[itemId].value=itemValue=itemValue?itemValue:”Unknown Item”;//Set default item value
Item[itemId].icon=itemIcon=itemIcon?itemIcon:”Unknown Item”;//Set default item icon
Item[itemId].solid=isSolid=false;//Items are not solid by default
Item[itemId].burnTime=isBurnTime=-999;//Default burn time
Item[itemId].canBeBurnedByFire=isCanBeBurnedByFire=false;//Items cannot burn unless specified
Item[itemId].burnTimePerTick=isBurnTimePerTick=-999;//Default burn time per tick
Item[itemId].isFood=isIsFood=false;//Items cannot be eaten unless specified
Item[itemId].foodHealAmount=isFoodHealAmount=-999;//Default heal amount when eaten
Item[itemId].foodSaturationAmount=isFoodSaturationAmount=-999;//Default saturation amount when eaten
Item[itemId].foodHungerRestoreAmount=isFoodHungerRestoreAmount=-999;//Default hunger restore amount when eaten
}
EntityItem.prototype=new Entity();
EntityItem.prototype.constructor=function(){return EntityItem;};
function EntityMob(eid,x,y,z,type,name,color,size,hurtTimer,maxhp,maxflyingrange,flyingrange,speedX,speedY,speedZ,hunger,hungermax)
{
Entity.call(this);
var mob=Mob[mobIndex];
mobIndex++;
mob.eid=eid;
mob.type=name?name:mob.type||”Unknown Mob”;
mob.x=x||mob.x||mob.spawnPointX;mob.y=y||mob.y||mob.spawnPointY;mob.z=z||mob.z||mob.spawnPointZ;
mob.color=color|mobsColor=mobsColor||(255<<16)|(255<<8)|255;
mob.size=size|mobsSize=mobsSize?size:mobsSize|32;
mob.hurtTimer=hurtTimer|mobsHurtTimer=mobsHurtTimer|hurtTimer|mobsHurtTimer|30;
mob.maxhp=maxhp|mobsMaxHp=mobsMaxHp|maxhp|mobsMaxHp|100;
mob.flyingRange=flyingrange|mobsFlyingRange=mobsFlyingRange?flyingrange:mobsFlyingRange|128;
mob.maxFlyingRange=maxflyingrange|mobsMaxFlyingRange=mobsMaxFlyingRange|maxflyingrange|mobsMaxFlyingRange|128;
if(speedX)speedX|=speedY|=speedZ|=mob.speedSpeed=mob.speedSpeed|=10000000;
speedX?s.mob.speedSpeed=s.mob.speedSpeed=s.mob.speedSpeed=s.mob.speedSpeed=s.mob.speedSpeed:s.mob.speedSpeed:s.mob.speedSpeed:s.mob.speedSpeed:s.mob.speedSpeed:s.mob.speedSpeed:moves.moving=random()*20000000-10000000;
speedY?s.mob.speedSpeed=s.mob.speedSpeed=s.mob.speedSpeed=s.mob.speedSpeed:s.mob.speedSpeed:s.movemove.move.move.move.move.moves.random()*20000000-10000000;
speedZ?s.movemove.moving.moving.moving.moving.moving.random()*20000000-10000000;
moves.hungers=movesHungers=movesHungers|hunger|hungermovesHungers|hungermovesHungers|20;
hungersmovesHungers.movesHungers.movesHungers.movesHungers.movesHungers.movesHungers.movesMovesMovesMovesMovesMovesMovesMovesMovesMovesMoves|hungermoveshungermoves|hungermoves|hungermovesmovesmovesmovemovemovemovemovemovemovemovemovesmovesmovemovemovemove|hungrermovesmovesmovemovemovemovemoveMoveMoveMoveMoveMoveMoveMoveMoveMovemoveMOVEMOVEMOVEMOVEMOVEMOVEMOVEMOVEMOVEMOVEMOVENew movesmovesnew movemovemovemovemovemovemovemovemove
hungermax=maxhungermax=maxhungermax|maxhungermax|maxhungermax|maxhungermax|maxhungermax|maxhungermax|maxhungermax|maxhungermax|80;
}
EntityMob.prototype=new Entity();
EntityMob.prototype.constructor=function(){return EntityMob;};
function GetNextAvailableEID()
{
for(var i=EIDs.length;i–;)//Loop through EIDs backwards
{
var eid=EIDs[i];//Get EID from array
if(!Entities[eid])//If EID does not exist then return it
return eid;
for(var j=i;j–;)//Loop through EIDs backwards again starting at i
{
var eid=EIDs[j];//Get EID from array
EIDs.splice(j
