Finished the implementation of the python code.

parkinsons_updrs.names

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+Parkinsons Telemonitoring Data Set  
+
+Abstract: Oxford Parkinson's Disease Telemonitoring Dataset
+
+============================================================
+
+Data Set Characteristics:  Multivariate
+Attribute Characteristics:  Integer, Real
+Associated Tasks:  Regression
+Number of Instances:  5875
+Number of Attributes:  26
+Area:  Life
+Date Donated:  2009-10-29
+
+============================================================
+
+SOURCE:
+
+The dataset was created by Athanasios Tsanas (tsanasthanasis '@' gmail.com) 
+and Max Little (littlem '@' physics.ox.ac.uk) of the University of Oxford, in 
+collaboration with 10 medical centers in the US and Intel Corporation who 
+developed the telemonitoring device to record the speech signals. The 
+original study used a range of linear and nonlinear regression methods to 
+predict the clinician's Parkinson's disease symptom score on the UPDRS scale.
+
+
+============================================================
+
+DATA SET INFORMATION:
+
+This dataset is composed of a range of biomedical voice measurements from 42 
+people with early-stage Parkinson's disease recruited to a six-month trial of 
+a telemonitoring device for remote symptom progression monitoring. The 
+recordings were automatically captured in the patient's homes.
+
+Columns in the table contain subject number, subject age, subject gender, 
+time interval from baseline recruitment date, motor UPDRS, total UPDRS, and 
+16 biomedical voice measures. Each row corresponds to one of 5,875 voice 
+recording from these individuals. The main aim of the data is to predict the 
+motor and total UPDRS scores ('motor_UPDRS' and 'total_UPDRS') from the 16 
+voice measures.
+
+The data is in ASCII CSV format. The rows of the CSV file contain an instance 
+corresponding to one voice recording. There are around 200 recordings per 
+patient, the subject number of the patient is identified in the first column. 
+For further information or to pass on comments, please contact Athanasios 
+Tsanas (tsanasthanasis '@' gmail.com) or Max Little (littlem '@' 
+physics.ox.ac.uk).
+
+Further details are contained in the following reference -- if you use this 
+dataset, please cite:
+Athanasios Tsanas, Max A. Little, Patrick E. McSharry, Lorraine O. Ramig (2009),
+'Accurate telemonitoring of Parkinson.s disease progression by non-invasive 
+speech tests',
+IEEE Transactions on Biomedical Engineering (to appear).
+
+Further details about the biomedical voice measures can be found in:
+Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2009),
+'Suitability of dysphonia measurements for telemonitoring of Parkinson's 
+disease',
+IEEE Transactions on Biomedical Engineering, 56(4):1015-1022 
+
+ 
+===========================================================
+
+ATTRIBUTE INFORMATION:
+
+subject# - Integer that uniquely identifies each subject
+age - Subject age
+sex - Subject gender '0' - male, '1' - female
+test_time - Time since recruitment into the trial. The integer part is the 
+number of days since recruitment.
+motor_UPDRS - Clinician's motor UPDRS score, linearly interpolated
+total_UPDRS - Clinician's total UPDRS score, linearly interpolated
+Jitter(%),Jitter(Abs),Jitter:RAP,Jitter:PPQ5,Jitter:DDP - Several measures of 
+variation in fundamental frequency
+Shimmer,Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,Shimmer:APQ11,Shimmer:DDA - 
+Several measures of variation in amplitude
+NHR,HNR - Two measures of ratio of noise to tonal components in the voice
+RPDE - A nonlinear dynamical complexity measure
+DFA - Signal fractal scaling exponent
+PPE - A nonlinear measure of fundamental frequency variation 
+
+
+===========================================================
+
+RELEVANT PAPERS:
+
+Little MA, McSharry PE, Hunter EJ, Ramig LO (2009),
+'Suitability of dysphonia measurements for telemonitoring of Parkinson's 
+disease',
+IEEE Transactions on Biomedical Engineering, 56(4):1015-1022
+
+Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM.
+'Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice 
+Disorder Detection',
+BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) 
+
+===========================================================
+
+CITATION REQUEST:
+
+If you use this dataset, please cite the following paper:
+A Tsanas, MA Little, PE McSharry, LO Ramig (2009)
+'Accurate telemonitoring of Parkinson.s disease progression by non-invasive 
+speech tests',
+IEEE Transactions on Biomedical Engineering (to appear).