diff options
| -rw-r--r-- | python/demo_feast_wrapper.py | 37 | ||||
| -rw-r--r-- | python/import_data.py | 18 |
2 files changed, 29 insertions, 26 deletions
diff --git a/python/demo_feast_wrapper.py b/python/demo_feast_wrapper.py index 6642ca8..4c965e3 100644 --- a/python/demo_feast_wrapper.py +++ b/python/demo_feast_wrapper.py @@ -1,36 +1,21 @@ #!/usr/bin/env python import feast import numpy as np +import import_data -################################################################## -################################################################## -################################################################## -def read_digits(fname='digit.txt'): - ''' - read_digits(fname='digit.txt') - - read a data file that contains the features and class labels. - each row of the file is a feature vector with the class - label appended. - ''' - import csv - - fw = csv.reader(open(fname,'rb'), delimiter='\t') - data = [] - for line in fw: - data.append( [float(x) for x in line] ) - data = np.array(data) - labels = data[:,len(data.transpose())-1] - data = data[:,:len(data.transpose())-1] - return data, labels -################################################################## -################################################################## -################################################################## + +print '---> Loading digit data' + +data_source = 'uniform' + + +if data_source == 'uniform': + data, labels = import_data.uniform_data() +elif data_source == 'digits': + data, labels = import_data.read_digits('digit.txt') -print '---> Loading digit data' -data, labels = read_digits('digit.txt') n_observations = len(data) # number of samples in the data set n_features = len(data.transpose()) # number of features in the data set n_select = 15 # how many features to select diff --git a/python/import_data.py b/python/import_data.py index c97ce7e..6d4bd9e 100644 --- a/python/import_data.py +++ b/python/import_data.py @@ -14,6 +14,7 @@ def read_digits(fname='digit.txt'): label appended. ''' import csv + import numpy as np fw = csv.reader(open(fname,'rb'), delimiter='\t') data = [] @@ -34,6 +35,23 @@ def read_digits(fname='digit.txt'): ################################################################## def uniform_data(n_observations = 1000, n_features = 50, n_relevant = 5): import numpy as np + xmax = 10 + xmin = 0 + data = np.random.randint(xmax + 1, size = (n_features, n_observations)) + labels = np.zeros(n_observations) + delta = n_relevant * (xmax - xmin) / 2.0 + + for m in range(n_observations): + zz = 0.0 + for k in range(n_relevant): + zz += data[k, m] + if zz > delta: + labels[m] = 1 + else: + labels[m] = 2 + data = data.transpose() + + return data, labels ################################################################## ################################################################## |