# Path: mimicus/config.py
# def _mkdir_p(path):
# def parse_config():
# 
# Path: mimicus/classifiers/RandomForest.py
# class sklearn_RF(RandomForestClassifier):
#     def __init__(self,
#                  n_estimators=1000, # Used by PDFrate
#                  criterion="gini",
#                  max_depth=None,
#                  min_samples_split=2,
#                  min_samples_leaf=1,
#                  max_features=43, # Used by PDFrate
#                  bootstrap=True,
#                  oob_score=False,
#                  n_jobs=-1, # Run in parallel
#                  random_state=None,
#                  verbose=0):
#     def decision_function(self, X):
#     def save_model(self, model_file):
#     def load_model(self, model_file):
# 
# 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 os
import unittest
import numpy
from mimicus import config
from mimicus.classifiers import RandomForest
from mimicus.tools import datasets
(at your option) any later version.

Mimicus is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with Mimicus.  If not, see <http://www.gnu.org/licenses/>.
##############################################################################
RandomForest_test.py

Created on May 17, 2013
'''




class RandomForest_Test(unittest.TestCase):
    '''
    Tests for the RandomForest class.
    '''
    
    def setUp(self):
        self.rf = RandomForest()
    
    def test_constructor(self):
        _ = RandomForest()
    
    def test_decision_function(self):

# Path: mimicus/config.py
# def _mkdir_p(path):
# def parse_config():
# 
# Path: mimicus/classifiers/RandomForest.py
# class sklearn_RF(RandomForestClassifier):
#     def __init__(self,
#                  n_estimators=1000, # Used by PDFrate
#                  criterion="gini",
#                  max_depth=None,
#                  min_samples_split=2,
#                  min_samples_leaf=1,
#                  max_features=43, # Used by PDFrate
#                  bootstrap=True,
#                  oob_score=False,
#                  n_jobs=-1, # Run in parallel
#                  random_state=None,
#                  verbose=0):
#     def decision_function(self, X):
#     def save_model(self, model_file):
#     def load_model(self, model_file):
# 
# 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/test/RandomForest_test.py
import os
import unittest
import numpy
from mimicus import config
from mimicus.classifiers import RandomForest
from mimicus.tools import datasets

(at your option) any later version.

Mimicus is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with Mimicus.  If not, see <http://www.gnu.org/licenses/>.
##############################################################################
RandomForest_test.py

Created on May 17, 2013
'''




class RandomForest_Test(unittest.TestCase):
    '''
    Tests for the RandomForest class.
    '''
    
    def setUp(self):
        self.rf = RandomForest()
    
    def test_constructor(self):
        _ = RandomForest()
    
    def test_decision_function(self):

        X, y, _ = datasets.csv2numpy(config.get('RandomForest_test', 'traindata'))