def initialize(ratio):
"""
Initialize the data entry and conversion to numerical values
Returns Training data and Testing Data
Input : Ratio of Testing to Training
X : Input Features
Y : Output
"""
input_file = 'mushrooms_bfgs.data'
input_test_file = ''
custom_delimiter = ','
proportion_factor = ratio
split = True
input_columns = range(1, 23)
output_column = 0
input_literal_columns = [1] * 23
input_label_mapping = {1:{'b':0, 'c':1, 'x':2, 'f':3, 'k':4, 's':5},
2:{'f':0, 'g':1, 'y':2, 's':3},
3:{'n':0, 'b':1, 'c':2, 'g':3, 'r':4, 'p':5, 'u':6,
'e':7, 'w':8, 'y':9},
4:{'t':0, 'f':1},
5:{'a':0, 'l':1, 'c':2, 'y':3, 'f':4, 'm':5, 'n':6, 'p':7, 's':8},
6:{'a':0, 'd':1, 'f':2, 'n':3},
7:{'c':0, 'w':1, 'd':2},
8:{'b':0, 'n':1},
9:{'k':0, 'n':1, 'b':2, 'h':3, 'g':4, 'r':5, 'o':6, 'p':7, 'u':8,
'e':9, 'w':10, 'y':11},
10:{'e':0, 't':1},
11:{'b':0, 'c':1, 'u':2, 'e':3, 'z':4, 'r':5, '?':6},
12:{'f':0, 'y':1, 'k':2, 's':3},
13:{'f':0, 'y':1, 'k':2, 's':3},
14:{'n':0, 'b':1, 'c':2, 'g':3, 'o':4, 'p':5, 'e':6, 'w':7, 'y':8},
15:{'n':0, 'b':1, 'c':2, 'g':3, 'o':4, 'p':5, 'e':6, 'w':7, 'y':8},
16:{'p':0, 'u':1},
17:{'n':0, 'o':1, 'w':2, 'y':3},
18:{'n':0, 'o':1, 't':2},
19:{'c':0, 'e':1, 'f':2, 'l':3, 'n':4, 'p':5, 's':6, 'z':7},
20:{'k':0, 'n':1, 'b':2, 'h':3, 'r':4, 'o':5, 'u':6, 'w':7, 'y':8},
21:{'a':0, 'c':1, 'n':2, 's':3, 'v':4, 'y':5},
22:{'g':0, 'l':1, 'm':2, 'p':3, 'u':4, 'w':5, 'd':6}}
output_literal = True
output_label_mapping = {'p':1, 'e':0}
return datareader.readInputData(
input_file, input_test_file, True, custom_delimiter,
proportion_factor, split, input_columns, output_column,
input_literal_columns, input_label_mapping, output_literal,
output_label_mapping)
},
22: {
'g': 0,
'l': 1,
'm': 2,
'p': 3,
'u': 4,
'w': 5,
'd': 6
}
}
output_literal = True
output_label_mapping = {'p': 1, 'e': 0}
(train_X, train_y, test_X, test_y) = datareader.readInputData(
input_file, input_test_file, True, custom_delimiter, proportion_factor,
split, input_columns, output_column, input_literal_columns,
input_label_mapping, output_literal, output_label_mapping)
print "Parsing complete!\n"
# Uncomment the following line to use PCA and to plot the training data set
#plot_data(train_X, train_y)
print "Optimizing...\n"
initial_values = numpy.zeros((len(train_X[0]), 1))
myargs = (train_X, train_y)
theta = scipy.optimize.fmin_bfgs(computeCost,
x0=initial_values,
args=myargs)
print "Final theta: "
print theta
raw_input("Press Enter to continue...")
print "Parsing input data..."
input_file = 'iris.data'
input_test_file = ''
custom_delimiter = ','
proportion_factor = float(1)/3
split = True
input_columns = range(4)
output_column = 4
input_literal_columns = [0] * 4
input_label_mapping = {}
output_literal = True
output_label_mapping = {'Iris-versicolor': 1, 'Iris-setosa': 0, 'Iris-virginica': 0}
(train_X, train_y, test_X, test_y) = datareader.readInputData(input_file, input_test_file, True, custom_delimiter,
proportion_factor, split, input_columns, output_column, input_literal_columns, input_label_mapping, output_literal, output_label_mapping)
print "Parsing complete!\n"
# Uncomment the following line to use PCA and to plot the training data set
#plot_data(train_X, train_y)
print "Optimizing...\n"
initial_thetas = numpy.zeros((train_X.shape[1], 1))
myargs = (train_X, train_y)
theta = scipy.optimize.fmin_bfgs(computeCost, x0=initial_thetas, args=myargs)
print "Final theta: "
print theta
check_test_data(test_X, test_y, theta)
