# Path: mimicus/tools/datasets.py
# def csv2numpy(csv_in):
# def numpy2csv(csv_out, X, y, file_names=None):
# def standardize_csv(csv_in, csv_out, standardizer=None):
#     TOTAL_ROWS = 0
#     X = numpy.array(numpy.zeros((TOTAL_ROWS, FeatureDescriptor.get_feature_count())), dtype=numpy.float64, order='C')


import random
import string
import tempfile
import threading
import numpy
import rpy2.rinterface as rinterface
import rpy2.robjects as robjects
from sklearn.base import BaseEstimator
from mimicus.tools import datasets
        self.model_trained = False
        self.persistent_model_Rname = 'savedrfmodel353'
    
    def get_model_name(self):
        return self.model_Rname
    
    @staticmethod
    def _get_new_R_name():
        '''
        Returns a randomly generated 7-character word consisting of lower 
        case characters that is unique in the current R global namespace, 
        i.e., it can be safely used as a variable name without overwriting 
        existing variables.
        '''
        while 1:
            name = ''.join(random.choice(string.lowercase) for _ in range(7))
            with _R_lock:
                if not robjects.r('exists("{var}")'.format(var=name))[0]:
                    robjects.r('{var} <- 0'.format(var=name))
                    return name
    
    def decision_function(self, X):
        '''
        Classifies novel data points using a trained model. Returns a 
        list of predictions, one per data point, giving the probability 
        of the given data point belonging to the positive class. 
        '''
        assert self.model_trained, 'Must train a model before classification'
        with _R_lock:
            with tempfile.NamedTemporaryFile() as tmpfile:

# Path: mimicus/tools/datasets.py
# def csv2numpy(csv_in):
# def numpy2csv(csv_out, X, y, file_names=None):
# def standardize_csv(csv_in, csv_out, standardizer=None):
#     TOTAL_ROWS = 0
#     X = numpy.array(numpy.zeros((TOTAL_ROWS, FeatureDescriptor.get_feature_count())), dtype=numpy.float64, order='C')


# Path: mimicus/classifiers/R_randomForest.py
import random
import string
import tempfile
import threading
import numpy
import rpy2.rinterface as rinterface
import rpy2.robjects as robjects
from sklearn.base import BaseEstimator
from mimicus.tools import datasets

        self.model_trained = False
        self.persistent_model_Rname = 'savedrfmodel353'
    
    def get_model_name(self):
        return self.model_Rname
    
    @staticmethod
    def _get_new_R_name():
        '''
        Returns a randomly generated 7-character word consisting of lower 
        case characters that is unique in the current R global namespace, 
        i.e., it can be safely used as a variable name without overwriting 
        existing variables.
        '''
        while 1:
            name = ''.join(random.choice(string.lowercase) for _ in range(7))
            with _R_lock:
                if not robjects.r('exists("{var}")'.format(var=name))[0]:
                    robjects.r('{var} <- 0'.format(var=name))
                    return name
    
    def decision_function(self, X):
        '''
        Classifies novel data points using a trained model. Returns a 
        list of predictions, one per data point, giving the probability 
        of the given data point belonging to the positive class. 
        '''
        assert self.model_trained, 'Must train a model before classification'
        with _R_lock:
            with tempfile.NamedTemporaryFile() as tmpfile:

                datasets.numpy2csv(tmpfile, X, numpy.zeros((X.shape[0],)))