Unsets the learning mode, to start inference.
487770
def _unsetLearningMode(self):
    

    for column in self.L4Columns:
      column.setParameter("learn", 0, False)
    for column in self.L6Columns:
      column.setParameter("learn", 0, False)

    for column in self.L2Columns:
      column.setParameter("learningMode", 0, False)
    for column in self.L5Columns:
      column.setParameter("learningMode", 0, False)
563529
public void reset() {
    this.result = UNDEF;
    this.var2index = new TreeMap<>();
    this.index2var = new TreeMap<>();
    switch (this.algorithm) {
      case WBO:
        this.solver = new WBO(this.configuration);
        break;
      case INC_WBO:
        this.solver = new IncWBO(this.configuration);
        break;
      case LINEAR_SU:
        this.solver = new LinearSU(this.configuration);
        break;
      case LINEAR_US:
        this.solver = new LinearUS(this.configuration);
        break;
      case MSU3:
        this.solver = new MSU3(this.configuration);
        break;
      case WMSU3:
        this.solver = new WMSU3(this.configuration);
        break;
      default:
        throw new IllegalArgumentException("Unknown MaxSAT algorithm: " + this.algorithm);
    }
  }
326599
def reset(self):
        
        self._x = self._start_x
        self._y = self._start_y
90347
def reset(self):

		

		Y, D, X = self.old_data['Y'], self.old_data['D'], self.old_data['X']
		self.raw_data = Data(Y, D, X)
		self.summary_stats = Summary(self.raw_data)
		self.propensity = None
		self.cutoff = None
		self.blocks = None
		self.strata = None
		self.estimates = Estimators()
1087630
def uninitialize(cls) -> None:
        """"""
        if not cls._initialized:
            return
        signal.signal(signal.SIGCHLD, cls._old_sigchld)
        cls._initialized = False
458235
def unset_env():
        """"""
        os.environ.pop('COV_CORE_SOURCE', None)
        os.environ.pop('COV_CORE_DATA_FILE', None)
        os.environ.pop('COV_CORE_CONFIG', None)
629788
def modes_off(self):
        """"""
        bm = self.fitsimage.get_bindmap()
        bm.reset_mode(self.fitsimage)
164755
def reset(self):
        ""
        self.hidden = [next(self.parameters()).data.new(0) for i in range(self.n_layers+1)]
406844
def _prune(self):
        """"""
        self.hypotheses = sorted(self.hypotheses,
                                 key=lambda e: e['probability'],
                                 reverse=True)[:self.k]
660577
private void disconnectClauses() {
        this.limits.reduceImportant = 0;
        for (int idx = 1; idx <= maxvar(); idx++) {
            for (int sign = -1; sign <= 1; sign += 2) {
                final int lit = sign * idx;
                watches(lit).release();
                occs(lit).release();
            }
        }
        this.dense = false;
    }
539621
def reset(self):
        """"""
        self.alpha_ = np.zeros(self._size, dtype=int)
        self.beta_ = np.zeros(self._size, dtype=int)
        self.theta_ = np.empty(self._size, dtype=float)
        if self.store_variance:
            self.var_theta_ = np.empty(self._size, dtype=float)
        if self.store_wp:
            self.theta_wp_ = np.empty(self._size, dtype=float)

        self._calc_theta()
        if self.store_variance:
            self._calc_var_theta()
11979
def cancel(self):
        """"""
        self._cancelled.set()
        if not self._dead:
            self._ready_sem.acquire()
            self._ready_sem.release()
110996
def removeFeatureSet(self, featureSet):
        
        q = models.Featureset.delete().where(
            models.Featureset.id == featureSet.getId())
        q.execute()
77926
public static void resetModelClass() {
		ModelFactory.modelClass = DefaultModelImpl.class;
    	System.err.println("[INFO] Reset model-class: " + ModelFactory.modelClass.getName());
	}
419739
public function reset(): void
    {
        $this->classifiers = [];
        $this->allLabels = [];
        $this->costValues = [];

        $this->resetBinary();
    }
58212
def reset( self ):
        
        dataSet = self.dataSet()
        if ( not dataSet ):
            dataSet = XScheme()
        
        dataSet.reset()
875055
def reset(self):
        """"""
        self.idx_annotations.setText('Load Annotation File...')
        self.idx_rater.setText('')

        self.annot = None
        self.dataset_markers = None

        # remove dataset marker
        self.idx_marker.clearContents()
        self.idx_marker.setRowCount(0)

        # remove summary statistics
        w1 = self.idx_summary.takeAt(1).widget()
        w2 = self.idx_summary.takeAt(1).widget()
        self.idx_summary.removeWidget(w1)
        self.idx_summary.removeWidget(w2)
        w1.deleteLater()
        w2.deleteLater()
        b1 = QGroupBox('Staging')
        b2 = QGroupBox('Signal quality')
        self.idx_summary.addWidget(b1)
        self.idx_summary.addWidget(b2)

        # remove annotations
        self.display_eventtype()
        self.update_annotations()

        self.parent.create_menubar()