Adjusted scripts

exercises/02450Toolbox_Python/Data/courses.txt

-2,5,6,7,8
-1,2,3,6,7,8
-2,4,6,8
-3,6,7
-2,6,7
-2,3,6,7,8

exercises/02450Toolbox_Python/Data/faithful.txt

-3.600000 79.000000
-1.800000 54.000000
-3.333000 74.000000
-2.283000 62.000000
-4.533000 85.000000
-2.883000 55.000000
-4.700000 88.000000
-3.600000 85.000000
-1.950000 51.000000
-4.350000 85.000000
-1.833000 54.000000
-3.917000 84.000000
-4.200000 78.000000
-1.750000 47.000000
-4.700000 83.000000
-2.167000 52.000000
-1.750000 62.000000
-4.800000 84.000000
-1.600000 52.000000
-4.250000 79.000000
-1.800000 51.000000
-1.750000 47.000000
-3.450000 78.000000
-3.067000 69.000000
-4.533000 74.000000
-3.600000 83.000000
-1.967000 55.000000
-4.083000 76.000000
-3.850000 78.000000
-4.433000 79.000000
-4.300000 73.000000
-4.467000 77.000000
-3.367000 66.000000
-4.033000 80.000000
-3.833000 74.000000
-2.017000 52.000000
-1.867000 48.000000
-4.833000 80.000000
-1.833000 59.000000
-4.783000 90.000000
-4.350000 80.000000
-1.883000 58.000000
-4.567000 84.000000
-1.750000 58.000000
-4.533000 73.000000
-3.317000 83.000000
-3.833000 64.000000
-2.100000 53.000000
-4.633000 82.000000
-2.000000 59.000000
-4.800000 75.000000
-4.716000 90.000000
-1.833000 54.000000
-4.833000 80.000000
-1.733000 54.000000
-4.883000 83.000000
-3.717000 71.000000
-1.667000 64.000000
-4.567000 77.000000
-4.317000 81.000000
-2.233000 59.000000
-4.500000 84.000000
-1.750000 48.000000
-4.800000 82.000000
-1.817000 60.000000
-4.400000 92.000000
-4.167000 78.000000
-4.700000 78.000000
-2.067000 65.000000
-4.700000 73.000000
-4.033000 82.000000
-1.967000 56.000000
-4.500000 79.000000
-4.000000 71.000000
-1.983000 62.000000
-5.067000 76.000000
-2.017000 60.000000
-4.567000 78.000000
-3.883000 76.000000
-3.600000 83.000000
-4.133000 75.000000
-4.333000 82.000000
-4.100000 70.000000
-2.633000 65.000000
-4.067000 73.000000
-4.933000 88.000000
-3.950000 76.000000
-4.517000 80.000000
-2.167000 48.000000
-4.000000 86.000000
-2.200000 60.000000
-4.333000 90.000000
-1.867000 50.000000
-4.817000 78.000000
-1.833000 63.000000
-4.300000 72.000000
-4.667000 84.000000
-3.750000 75.000000
-1.867000 51.000000
-4.900000 82.000000
-2.483000 62.000000
-4.367000 88.000000
-2.100000 49.000000
-4.500000 83.000000
-4.050000 81.000000
-1.867000 47.000000
-4.700000 84.000000
-1.783000 52.000000
-4.850000 86.000000
-3.683000 81.000000
-4.733000 75.000000
-2.300000 59.000000
-4.900000 89.000000
-4.417000 79.000000
-1.700000 59.000000
-4.633000 81.000000
-2.317000 50.000000
-4.600000 85.000000
-1.817000 59.000000
-4.417000 87.000000
-2.617000 53.000000
-4.067000 69.000000
-4.250000 77.000000
-1.967000 56.000000
-4.600000 88.000000
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-1.917000 45.000000
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-2.267000 55.000000
-4.650000 90.000000
-1.867000 45.000000
-4.167000 83.000000
-2.800000 56.000000
-4.333000 89.000000
-1.833000 46.000000
-4.383000 82.000000
-1.883000 51.000000
-4.933000 86.000000
-2.033000 53.000000
-3.733000 79.000000
-4.233000 81.000000
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-4.533000 82.000000
-4.817000 77.000000
-4.333000 76.000000
-1.983000 59.000000
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-2.017000 49.000000
-5.100000 96.000000
-1.800000 53.000000
-5.033000 77.000000
-4.000000 77.000000
-2.400000 65.000000
-4.600000 81.000000
-3.567000 71.000000
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-4.500000 81.000000
-4.083000 93.000000
-1.800000 53.000000
-3.967000 89.000000
-2.200000 45.000000
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-2.000000 58.000000
-3.833000 78.000000
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-2.367000 63.000000
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-1.933000 52.000000
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-1.917000 49.000000
-2.083000 57.000000
-4.583000 77.000000
-3.333000 68.000000
-4.167000 81.000000
-4.333000 81.000000
-4.500000 73.000000
-2.417000 50.000000
-4.000000 85.000000
-4.167000 74.000000
-1.883000 55.000000
-4.583000 77.000000
-4.250000 83.000000
-3.767000 83.000000
-2.033000 51.000000
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-4.083000 84.000000
-1.833000 46.000000
-4.417000 83.000000
-2.183000 55.000000
-4.800000 81.000000
-1.833000 57.000000
-4.800000 76.000000
-4.100000 84.000000
-3.966000 77.000000
-4.233000 81.000000
-3.500000 87.000000
-4.366000 77.000000
-2.250000 51.000000
-4.667000 78.000000
-2.100000 60.000000
-4.350000 82.000000
-4.133000 91.000000
-1.867000 53.000000
-4.600000 78.000000
-1.783000 46.000000
-4.367000 77.000000
-3.850000 84.000000
-1.933000 49.000000
-4.500000 83.000000
-2.383000 71.000000
-4.700000 80.000000
-1.867000 49.000000
-3.833000 75.000000
-3.417000 64.000000
-4.233000 76.000000
-2.400000 53.000000
-4.800000 94.000000
-2.000000 55.000000
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-1.867000 50.000000
-4.267000 82.000000
-1.750000 54.000000
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-4.000000 78.000000
-4.117000 79.000000
-4.083000 78.000000
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-3.917000 70.000000
-4.550000 79.000000
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-2.417000 54.000000
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-4.933000 86.000000
-2.900000 63.000000
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-2.133000 67.000000
-4.350000 74.000000
-2.200000 54.000000
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-3.567000 73.000000
-4.500000 73.000000
-4.150000 88.000000
-3.817000 80.000000
-3.917000 71.000000
-4.450000 83.000000
-2.000000 56.000000
-4.283000 79.000000
-4.767000 78.000000
-4.533000 84.000000
-1.850000 58.000000
-4.250000 83.000000
-1.983000 43.000000
-2.250000 60.000000
-4.750000 75.000000
-4.117000 81.000000
-2.150000 46.000000
-4.417000 90.000000
-1.817000 46.000000
-4.467000 74.000000

exercises/02450Toolbox_Python/Data/iris.csv

-"Sepal Length","Sepal Width","Petal Length","Petal Width","Type"
-5.1,3.5,1.4,0.2,"Iris-setosa"
-4.9,3,1.4,0.2,"Iris-setosa"
-4.7,3.2,1.3,0.2,"Iris-setosa"
-4.6,3.1,1.5,0.2,"Iris-setosa"
-5,3.6,1.4,0.2,"Iris-setosa"
-5.4,3.9,1.7,0.4,"Iris-setosa"
-4.6,3.4,1.4,0.3,"Iris-setosa"
-5,3.4,1.5,0.2,"Iris-setosa"
-4.4,2.9,1.4,0.2,"Iris-setosa"
-4.9,3.1,1.5,0.1,"Iris-setosa"
-5.4,3.7,1.5,0.2,"Iris-setosa"
-4.8,3.4,1.6,0.2,"Iris-setosa"
-4.8,3,1.4,0.1,"Iris-setosa"
-4.3,3,1.1,0.1,"Iris-setosa"
-5.8,4,1.2,0.2,"Iris-setosa"
-5.7,4.4,1.5,0.4,"Iris-setosa"
-5.4,3.9,1.3,0.4,"Iris-setosa"
-5.1,3.5,1.4,0.3,"Iris-setosa"
-5.7,3.8,1.7,0.3,"Iris-setosa"
-5.1,3.8,1.5,0.3,"Iris-setosa"
-5.4,3.4,1.7,0.2,"Iris-setosa"
-5.1,3.7,1.5,0.4,"Iris-setosa"
-4.6,3.6,1,0.2,"Iris-setosa"
-5.1,3.3,1.7,0.5,"Iris-setosa"
-4.8,3.4,1.9,0.2,"Iris-setosa"
-5,3,1.6,0.2,"Iris-setosa"
-5,3.4,1.6,0.4,"Iris-setosa"
-5.2,3.5,1.5,0.2,"Iris-setosa"
-5.2,3.4,1.4,0.2,"Iris-setosa"
-4.7,3.2,1.6,0.2,"Iris-setosa"
-4.8,3.1,1.6,0.2,"Iris-setosa"
-5.4,3.4,1.5,0.4,"Iris-setosa"
-5.2,4.1,1.5,0.1,"Iris-setosa"
-5.5,4.2,1.4,0.2,"Iris-setosa"
-4.9,3.1,1.5,0.1,"Iris-setosa"
-5,3.2,1.2,0.2,"Iris-setosa"
-5.5,3.5,1.3,0.2,"Iris-setosa"
-4.9,3.1,1.5,0.1,"Iris-setosa"
-4.4,3,1.3,0.2,"Iris-setosa"
-5.1,3.4,1.5,0.2,"Iris-setosa"
-5,3.5,1.3,0.3,"Iris-setosa"
-4.5,2.3,1.3,0.3,"Iris-setosa"
-4.4,3.2,1.3,0.2,"Iris-setosa"
-5,3.5,1.6,0.6,"Iris-setosa"
-5.1,3.8,1.9,0.4,"Iris-setosa"
-4.8,3,1.4,0.3,"Iris-setosa"
-5.1,3.8,1.6,0.2,"Iris-setosa"
-4.6,3.2,1.4,0.2,"Iris-setosa"
-5.3,3.7,1.5,0.2,"Iris-setosa"
-5,3.3,1.4,0.2,"Iris-setosa"
-7,3.2,4.7,1.4,"Iris-versicolor"
-6.4,3.2,4.5,1.5,"Iris-versicolor"
-6.9,3.1,4.9,1.5,"Iris-versicolor"
-5.5,2.3,4,1.3,"Iris-versicolor"
-6.5,2.8,4.6,1.5,"Iris-versicolor"
-5.7,2.8,4.5,1.3,"Iris-versicolor"
-6.3,3.3,4.7,1.6,"Iris-versicolor"
-4.9,2.4,3.3,1,"Iris-versicolor"
-6.6,2.9,4.6,1.3,"Iris-versicolor"
-5.2,2.7,3.9,1.4,"Iris-versicolor"
-5,2,3.5,1,"Iris-versicolor"
-5.9,3,4.2,1.5,"Iris-versicolor"
-6,2.2,4,1,"Iris-versicolor"
-6.1,2.9,4.7,1.4,"Iris-versicolor"
-5.6,2.9,3.6,1.3,"Iris-versicolor"
-6.7,3.1,4.4,1.4,"Iris-versicolor"
-5.6,3,4.5,1.5,"Iris-versicolor"
-5.8,2.7,4.1,1,"Iris-versicolor"
-6.2,2.2,4.5,1.5,"Iris-versicolor"
-5.6,2.5,3.9,1.1,"Iris-versicolor"
-5.9,3.2,4.8,1.8,"Iris-versicolor"
-6.1,2.8,4,1.3,"Iris-versicolor"
-6.3,2.5,4.9,1.5,"Iris-versicolor"
-6.1,2.8,4.7,1.2,"Iris-versicolor"
-6.4,2.9,4.3,1.3,"Iris-versicolor"
-6.6,3,4.4,1.4,"Iris-versicolor"
-6.8,2.8,4.8,1.4,"Iris-versicolor"
-6.7,3,5,1.7,"Iris-versicolor"
-6,2.9,4.5,1.5,"Iris-versicolor"
-5.7,2.6,3.5,1,"Iris-versicolor"
-5.5,2.4,3.8,1.1,"Iris-versicolor"
-5.5,2.4,3.7,1,"Iris-versicolor"
-5.8,2.7,3.9,1.2,"Iris-versicolor"
-6,2.7,5.1,1.6,"Iris-versicolor"
-5.4,3,4.5,1.5,"Iris-versicolor"
-6,3.4,4.5,1.6,"Iris-versicolor"
-6.7,3.1,4.7,1.5,"Iris-versicolor"
-6.3,2.3,4.4,1.3,"Iris-versicolor"
-5.6,3,4.1,1.3,"Iris-versicolor"
-5.5,2.5,4,1.3,"Iris-versicolor"
-5.5,2.6,4.4,1.2,"Iris-versicolor"
-6.1,3,4.6,1.4,"Iris-versicolor"
-5.8,2.6,4,1.2,"Iris-versicolor"
-5,2.3,3.3,1,"Iris-versicolor"
-5.6,2.7,4.2,1.3,"Iris-versicolor"
-5.7,3,4.2,1.2,"Iris-versicolor"
-5.7,2.9,4.2,1.3,"Iris-versicolor"
-6.2,2.9,4.3,1.3,"Iris-versicolor"
-5.1,2.5,3,1.1,"Iris-versicolor"
-5.7,2.8,4.1,1.3,"Iris-versicolor"
-6.3,3.3,6,2.5,"Iris-virginica"
-5.8,2.7,5.1,1.9,"Iris-virginica"
-7.1,3,5.9,2.1,"Iris-virginica"
-6.3,2.9,5.6,1.8,"Iris-virginica"
-6.5,3,5.8,2.2,"Iris-virginica"
-7.6,3,6.6,2.1,"Iris-virginica"
-4.9,2.5,4.5,1.7,"Iris-virginica"
-7.3,2.9,6.3,1.8,"Iris-virginica"
-6.7,2.5,5.8,1.8,"Iris-virginica"
-7.2,3.6,6.1,2.5,"Iris-virginica"
-6.5,3.2,5.1,2,"Iris-virginica"
-6.4,2.7,5.3,1.9,"Iris-virginica"
-6.8,3,5.5,2.1,"Iris-virginica"
-5.7,2.5,5,2,"Iris-virginica"
-5.8,2.8,5.1,2.4,"Iris-virginica"
-6.4,3.2,5.3,2.3,"Iris-virginica"
-6.5,3,5.5,1.8,"Iris-virginica"
-7.7,3.8,6.7,2.2,"Iris-virginica"
-7.7,2.6,6.9,2.3,"Iris-virginica"
-6,2.2,5,1.5,"Iris-virginica"
-6.9,3.2,5.7,2.3,"Iris-virginica"
-5.6,2.8,4.9,2,"Iris-virginica"
-7.7,2.8,6.7,2,"Iris-virginica"
-6.3,2.7,4.9,1.8,"Iris-virginica"
-6.7,3.3,5.7,2.1,"Iris-virginica"
-7.2,3.2,6,1.8,"Iris-virginica"
-6.2,2.8,4.8,1.8,"Iris-virginica"
-6.1,3,4.9,1.8,"Iris-virginica"
-6.4,2.8,5.6,2.1,"Iris-virginica"
-7.2,3,5.8,1.6,"Iris-virginica"
-7.4,2.8,6.1,1.9,"Iris-virginica"
-7.9,3.8,6.4,2,"Iris-virginica"
-6.4,2.8,5.6,2.2,"Iris-virginica"
-6.3,2.8,5.1,1.5,"Iris-virginica"
-6.1,2.6,5.6,1.4,"Iris-virginica"
-7.7,3,6.1,2.3,"Iris-virginica"
-6.3,3.4,5.6,2.4,"Iris-virginica"
-6.4,3.1,5.5,1.8,"Iris-virginica"
-6,3,4.8,1.8,"Iris-virginica"
-6.9,3.1,5.4,2.1,"Iris-virginica"
-6.7,3.1,5.6,2.4,"Iris-virginica"
-6.9,3.1,5.1,2.3,"Iris-virginica"
-5.8,2.7,5.1,1.9,"Iris-virginica"
-6.8,3.2,5.9,2.3,"Iris-virginica"
-6.7,3.3,5.7,2.5,"Iris-virginica"
-6.7,3,5.2,2.3,"Iris-virginica"
-6.3,2.5,5,1.9,"Iris-virginica"
-6.5,3,5.2,2,"Iris-virginica"
-6.2,3.4,5.4,2.3,"Iris-virginica"
-5.9,3,5.1,1.8,"Iris-virginica"

exercises/02450Toolbox_Python/Data/messy_data/README.txt

-***************************************************************************
-***************************************************************************
-***				messy_data				***
-***************************************************************************
-***************************************************************************
-This dataset is an adaption of an existing dataset to highlight some common
-issues (or variants of them) that one might face across various datasets.
-This is not real data, but is based on values from the Auto-Mpg Data. 
-The original data was obtained from:
-	https://archive.ics.uci.edu/ml/datasets/auto+mpg
-but was modified to include some formatting issues as well as removing some 
-values. 
-Missing values in the original dataset were sometimes denoted 
-with a question mark. Some missing values were introduced, too. 
-Specifically zeroes in the attributes mpg and displacement can be 
-considered missing values.
-
-For reference, the description of the original dataset is provided below.
-
-***************************************************************************
-***************************************************************************
-***			Original dataset description			***
-***************************************************************************
-***************************************************************************
-1. Title: Auto-Mpg Data
-
-2. Sources:
-   (a) Origin:  This dataset was taken from the StatLib library which is
-                maintained at Carnegie Mellon University. The dataset was 
-                used in the 1983 American Statistical Association Exposition.
-   (c) Date: July 7, 1993
-
-3. Past Usage:
-    -  See 2b (above)
-    -  Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning.
-       In Proceedings on the Tenth International Conference of Machine 
-       Learning, 236-243, University of Massachusetts, Amherst. Morgan
-       Kaufmann.
-
-4. Relevant Information:
-
-   This dataset is a slightly modified version of the dataset provided in
-   the StatLib library.  In line with the use by Ross Quinlan (1993) in
-   predicting the attribute "mpg", 8 of the original instances were removed 
-   because they had unknown values for the "mpg" attribute.  The original 
-   dataset is available in the file "auto-mpg.data-original".
-
-   "The data concerns city-cycle fuel consumption in miles per gallon,
-    to be predicted in terms of 3 multivalued discrete and 5 continuous
-    attributes." (Quinlan, 1993)
-
-5. Number of Instances: 398
-
-6. Number of Attributes: 9 including the class attribute
-
-7. Attribute Information:
-
-    1. mpg:           continuous
-    2. cylinders:     multi-valued discrete
-    3. displacement:  continuous
-    4. horsepower:    continuous
-    5. weight:        continuous
-    6. acceleration:  continuous
-    7. model year:    multi-valued discrete
-    8. origin:        multi-valued discrete
-    9. car name:      string (unique for each instance)
-
-8. Missing Attribute Values:  horsepower has 6 missing values
-
-

exercises/02450Toolbox_Python/Data/messy_data/messy_data.data

-messy_data
-mpg	cylinders	displacement	horsepower	weight	acceleration	modelyear	origin	carname
-mpg	cyl	disp	hp	w	acc	yr	org	name
-18	8	?	130	3'504	12.0	70	1	chevrolet chevelle malibu
-15	8	350	165	3'693	11,5	70	1	buick skylark 320
-18	8	?	150	3'436	11.0	70	1	plymouth satellite
-16	8	?	150	3'433	12.0	70	1	amc rebel sst
-17	8	0	140	3'449	10,5	70	1	ford torino
-15	8	429	198	4'341	10.0	70	1	ford galaxie 500
-14	8	454	220	4'354	9.0	70	1	chevrolet impala
-14	8	?	215	4312	8,5	70	1	plymouth fury iii
-14	8	455	225	4425	10.0	70	1	pontiac catalina
-15	8	390	190	3'850	8,5	70	1	amc ambassador dpl
-15	8	0	170	3'563	10.0	70	1	dodge challenger se
-14	8	?	160	3'609	8.0	70	1	plymouth 'cuda 340
-99	8	?	150	3'761	9,5	70	1	chevrolet monte carlo
-14	8	?	225	3'086	10.0	70	1	buick estate wagon (sw)
-24	4	113	95	2'372	15.0	70	3	toyota corona mark ii
-22	6		95	2'833	15,5	70	1	plymouth duster
-0	6	199	97	2'774	15,5	70	1	amc hornet
-21	6	?	85	2'587	16.0	70	1	ford maverick
-27	4	97	88	2'130	14,5	70	3	datsun pl510
-26	4		46	1'835	20,5	70	2	volkswagen 1131 deluxe sedan
-33	4	105	74	2190	14.2	81	2	volkswagen jetta
-33.7	4	107	75	2210	14.4	81	3	honda prelude
-32.4	4	108	75	2350	16.8	81	3	toyota corolla
-32.9	4	119	100	2615	14.8	81	3	datsun 200sx
-31.6	4	120	74	2635	18.3	81	3	mazda 626
-28.1	4	141	80	3'230	20.4	81	2	peugeot 505s turbo diesel
-30.7	6	145	76	3'160	19.6	81	2	volvo diesel
-0	6	168	116	2'900	12.6	81	3	toyota cressida
-24.2	6	146	120	2'930	13.8	81	3	datsun 810 maxima
-

exercises/02450Toolbox_Python/Data/readme_male_female_data.txt

-You may use the lists of names for any purpose, so long as credit is given
-in any published work. You may also redistribute the list if you
-provide the recipients with a copy of this README file. The lists are
-not in the public domain (I retain the copyright on the lists) but are
-freely redistributable.
-
-If you have any additions to the lists of names, I would appreciate
-receiving them.
-
-My email address is mkant+@cs.cmu.edu.
-
-Mark Kantrowitz

exercises/02450Toolbox_Python/Data/stopWords.txt

-a
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-would
-x
-y
-year
-years
-yet
-you
-your
-yours
-yourself
-yourselves
-z
-zero
\ No newline at end of file