Functions
def	create_ROC
def	extract_data
def	process
Variables
list	ACT_LIST = ['WINCE', 'NOD', 'SHAKE', 'JOY', "FEAR", "SUPRISE", "ANGER", "DISGUST", "SADNESS"]
dictionary	ACTIONS
list	all_tpr = []
tuple	args = parser.parse_args()
tuple	cv = StratifiedKFold(y, k=9)
	Code below modified from http://scikit-learn.org/stable/auto_examples/plot_roc_crossval.html#example-plot-roc-crossval-py.
dictionary	DEGREES
string	help = "One or more training data files to process"
string	label = 'Mean ROC (area = %0.2f)'
tuple	mean_auc = auc(mean_fpr, mean_tpr)
tuple	mean_fpr = np.linspace(0, 1, n_samples)
float	mean_tpr = 0.0
tuple	parser
tuple	probas_ = classifier.fit(X[train], y[train])
tuple	roc_auc = auc(fpr, tpr)

Function Documentation

def data_parser.create_ROC ( filename )

Definition at line 170 of file data_parser.py.

def data_parser.extract_data ( files )

Definition at line 34 of file data_parser.py.

def data_parser.process	(	files,
		SVM_DATA_FILE,
		WINDOW_DUR,
		MAG_THRESH,
		plot
	)

Definition at line 43 of file data_parser.py.

Variable Documentation

list data_parser::ACT_LIST = ['WINCE', 'NOD', 'SHAKE', 'JOY', "FEAR", "SUPRISE", "ANGER", "DISGUST", "SADNESS"]

Definition at line 31 of file data_parser.py.

dictionary data_parser::ACTIONS

Initial value:

00001 {'WINCE'  : [0,0,0],
00002             'SMILE' : [0.5,0,0] ,
00003             'FROWN' : [0,0.5,0],
00004             'LAUGH' : [0,0,0.5],
00005             'GLARE' : [0.5,0.5,0],
00006             'NOD'   : [0.5,0,0.5],
00007             'SHAKE' : [0,0.5,0.5],
00008             'REQUEST FOR BOARD': [0.5,0.5,0.5],
00009             'EYE-ROLL':[1,0,0],
00010             'JOY'   :  [0,1,0],
00011             'SUPRISE': [0,0,1],
00012             'FEAR'  :  [1,1,0],
00013             'ANGER' :  [0,1,1],
00014             'DISGUST': [1,0,1],
00015             'SADNESS': [0.5,0,0]}

Definition at line 16 of file data_parser.py.

list data_parser::all_tpr = []

Definition at line 207 of file data_parser.py.

tuple data_parser::args = parser.parse_args()

Definition at line 252 of file data_parser.py.

tuple data_parser::cv = StratifiedKFold(y, k=9)

Code below modified from http://scikit-learn.org/stable/auto_examples/plot_roc_crossval.html#example-plot-roc-crossval-py.

Classification and ROC analysis Run classifier with crossvalidation and plot ROC curves

Definition at line 203 of file data_parser.py.

dictionary data_parser::DEGREES

Initial value:

00001 {'WEAK'   : 0.33,
00002            'AVERAGE': 0.66,
00003            'STRONG' : 1.0}

Definition at line 12 of file data_parser.py.

string data_parser::help = "One or more training data files to process"

Definition at line 241 of file data_parser.py.

string data_parser::label = 'Mean ROC (area = %0.2f)'

Definition at line 224 of file data_parser.py.

tuple data_parser::mean_auc = auc(mean_fpr, mean_tpr)

Definition at line 222 of file data_parser.py.

tuple data_parser::mean_fpr = np.linspace(0, 1, n_samples)

Definition at line 206 of file data_parser.py.

float data_parser::mean_tpr = 0.0

Definition at line 205 of file data_parser.py.

tuple data_parser::parser

Initial value:

00001 argparse.ArgumentParser(
00002                 description="Process raw wouse training data to output plots,"
00003                             "statistics, and SVM-ready formatted data",
00004                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)

Definition at line 236 of file data_parser.py.

tuple data_parser::probas_ = classifier.fit(X[train], y[train])

Definition at line 210 of file data_parser.py.

tuple data_parser::roc_auc = auc(fpr, tpr)

Definition at line 215 of file data_parser.py.

Functions

Variables

Function Documentation

Variable Documentation