haydarai

List<Post> posts = ...; // Fill the list with data
List<Post> filteredPost = new ArrayList<>();

// Iterate through the collection
for (Post post : posts) {
    if (post.getAuthor().equals("Haydar") // The filter condition
        filteredPost.add(post); // Adding the filtered item to the filtered collection
}

haydarai

viewHolder.deleteButton.setOnClickListener(view -> delete(viewHolder.getAdapterPosition()));

haydarai

@Override
public void onBindViewHolder(PostAdapter.ViewHolder holder,
	int position) { // The `position` sometimes is not updated according to the latest update
	
	/**
	  *	We initialize our view for the current item here
	 **/
	 
	 /**
	   * We want to set our delete button to run delete method.

haydarai

List<Post> posts = ...; // Fill the list with initial data
PostAdapter adapter = new PostAdapter(this, posts); // Initialize the RecyclerView Adapter
recyclerView.setAdapter(adapter); // Set adapter to the RecyclerView

/**
  * Running some operation that modify the `posts` list
 **/

adapter.notifyDataSetChanged; // Notify the adapter that the `posts` list already changed,
                              // it can also substituted with `notifyItemInserted`,

haydarai

# custom Android Studio VM options

#
# *DO NOT* modify this file directly. If there is a value that you would like to override,
# please add it to your user specific configuration file.
#
# See http://tools.android.com/tech-docs/configuration
#
-server
-Xms1G

haydarai

org.gradle.parallel=true
org.gradle.daemon=true

haydarai

cv_scores = cross_val_score(dtc, all_inputs, all_classes)
sns.distplot(cv_scores)
plt.title('Average score: {}'.format(np.mean(cv_scores)))

haydarai

from sklearn.grid_search import GridSearchCV

dtc = DecisionTreeClassifier()

parameter_grid = {'criterion': ['gini', 'entropy'],
                  'splitter': ['best', 'random'],
                  'max_depth': [1, 2, 3, 4, 5],
                  'max_features': [1, 2, 3, 4]}

cross_validation = StratifiedKFold(all_classes, n_folds=10)

haydarai

from sklearn.cross_validation import cross_val_score

dtc = DecisionTreeClassifier()
cv_scores = cross_val_score(dtc, all_inputs, all_classes, cv=10)
sns.distplot(cv_scores)
plt.title('Average score: {}'.format(np.mean(cv_scores)))

haydarai

model_accuracies = []

for i in range(1000):
    (training_inputs,
     testing_inputs,
     training_classes,
     testing_classes) = train_test_split(all_inputs, all_classes, train_size=0.7)
    
    decision_tree_classifier = DecisionTreeClassifier()
    decision_tree_classifier.fit(training_inputs, training_classes)