comment tools, clean hw6

hw5.py

@@ -157,7 +157,6 @@ def run_log_regression():
     eps = 0.01
 
     for i in range(nb_runs):
-
         # generate random function
         l = randomline()
         f = target_random_function(l)

hw6.py

@@ -11,6 +11,8 @@
 from tools import target_vector
 from tools import input_data_matrix
 from tools import pseudo_inverse
+from tools import data_from_file
+from tools import linear_regression
 
 KA = -3
 LAMBDA = 10**KA
@@ -75,58 +77,55 @@ def run_nonlineartransformation(indata,outdata):
     t_set_trans = transform_t_set(indata)
     wtrans,Xtrans,ytrans = linear_regression(N_points,t_set_trans)
     print '-2-'
+    print 'Linear regression on training set after non linear transformation:'
     Eintrans = compute_Ein(wtrans,Xtrans,ytrans)
     Eouttrans = compute_Eout_nonlineartrans(wtrans,outdata)
-    print Eintrans
-    print Eouttrans
+    print 'in sample classification error: %s'%(Eintrans)
+    print 'out of sample classification error: %s'%(Eouttrans)
     print '-3-'
+    print 'Adding weight decay to linear regression with lambda = 10k and k = -3'
     w_decay = compute_weight_decay(wtrans,t_set_trans,Xtrans,ytrans,-3)
     Eintrans_decay = compute_Ein(w_decay,Xtrans,ytrans)
     Eouttrans_decay=compute_Eout_nonlineartrans(w_decay,outdata)
-    print Eintrans_decay
-    print Eouttrans_decay
+    print 'in sample classification error:%s'%(Eintrans_decay)
+    print 'out of sample classification error: %s'%(Eouttrans_decay)
     print '-4-'
+    print 'Using now k = 3'
     w_decay = compute_weight_decay(wtrans,t_set_trans,Xtrans,ytrans,3)
     Eintrans_decay = compute_Ein(w_decay,Xtrans,ytrans)
     Eouttrans_decay=compute_Eout_nonlineartrans(w_decay,outdata)
-    print Eintrans_decay
-    print Eouttrans_decay
+    print 'in sample classification error: %s'%(Eintrans_decay)
+    print 'out of sample classification error: %s'%(Eouttrans_decay)
     print '-5-'
-    for k in [2,1,0,-1,-2]:
+    Ks = [2,1,0,-1,-2]
+    print 'searching the lowest out of sample classification error for the following k values.'
+    print 'k in (%s)'%(str(Ks))
+    for k in Ks:
             w_decay = compute_weight_decay(wtrans,t_set_trans,Xtrans,ytrans,k)
             Eintrans_decay = compute_Ein(w_decay,Xtrans,ytrans)
             Eouttrans_decay=compute_Eout_nonlineartrans(w_decay,outdata)
-            print Eintrans_decay
-            print Eouttrans_decay
+            print 'K : %s'%(k)
+            print 'in sample classification error: %s'%(Eintrans_decay)
+            print 'out of sample classification error: %s'%(Eouttrans_decay)
     print '-6-'
+    print 'searching the minimum out of sample classification error by varying k in the integer values.'
     mink = 999
     minEout = 999
-    for k in range(-100,100):
+    for k in range(-200,200):
         w_decay = compute_weight_decay(wtrans,t_set_trans,Xtrans,ytrans,k)
         Eintrans_decay = compute_Ein(w_decay,Xtrans,ytrans)
         Eout_decay=compute_Eout_nonlineartrans(w_decay,outdata)
         if Eout_decay < minEout:
             minEout = Eout_decay
             mink = k
-    print k
-    print minEout
-
-def getData(filename):
-    datafile = open(filename, 'r')
-    data = []
-    for line in datafile:
-        split = line.split()
-        x1 = float(split[0])
-        x2 = float(split[1])
-        y = float(split[2])
-        data.append([ [x1,x2],y ])
-    return data
+    print 'K: %s'%(k)
+    print 'out of sample classification error: %s'%(minEout)
 
 def tests():
     print '-1-'
-    print '-2-'
-    indata  = getData('in.dta')
-    outdata = getData('out.dta')
+    indata  = data_from_file('in.dta')
+    outdata = data_from_file('out.dta')
     run_nonlineartransformation(indata,outdata)
     print '-8-'
-    demo()
+    print '-9-'
+    print '-10-'

hw7.py

@@ -1,3 +1,7 @@
+from math import sqrt
+
+from numpy import dot
+from numpy import sign
 
 from tools import linear_regression
 from tools import target_vector
@@ -6,12 +10,9 @@
 
 from hw1 import evaluate_diff_f_g
 
-from math import sqrt
-
-from numpy import dot
-from numpy import sign
-
 def getData(filename):
+    '''returns a data set with format [[x1,x2],y]
+    Note: To be moved to Tools'''
     datafile = open(filename, 'r')
     data = []
     for line in datafile:

tools.py

@@ -7,10 +7,13 @@
 
 
 def data_interval(low_b,high_b,N=100):
+    '''returns a vector of (N) values. 
+    Values are uniformly distributed between low boundary (low_b) and high boundary (high_b)'''
     d = []
     for i in range(N):
         d.append(uniform(low_b,high_b))
     return d
+
 def data(N = 10):
     'return N random points (x1,x2)'
     d = []
@@ -20,8 +23,20 @@ def data(N = 10):
         d.append([x,y])
     return d
 
+def data_from_file(filepath):
+    'from a filepath returns a dataset with the form [[x1,x2],y]'
+    datafile = open(filepath, 'r')
+    data = []
+    for line in datafile:
+        split = line.split()
+        x1 = float(split[0])
+        x2 = float(split[1])
+        y = float(split[2])
+        data.append([ [x1,x2],y ])
+    return data
+
 def randomline():
-    'computes a random line and returns a and b params: y = ax + b'
+    'computes a random line and returns [a,b] : y = ax + b'
     x1 = uniform(-1,1)
     y1 = uniform(-1,1)
     x2 = uniform(-1,1)
@@ -31,13 +46,19 @@ def randomline():
     b = y1 - a*x1
     return [a,b] # a*x + b
 
-def target_function(l):
-    # print 'Target function: %s x + %s' %(l[0], l[1]
-    f = lambda x: l[0]*x + l[1]
+def target_function(coords):
+    'from a coordinate input [a,b] returns the function a*x + b'
+    f = lambda x: coords[0]*x + coords[1]
     return f
 
-def target_random_function(l):
-    func = target_function(l)
+def target_random_function(coords):
+    '''
+    description: from a coordinate (coords) with the format [a,b] generated a random function.
+    - coord: a list of the form [a,b]
+    - returns: the generated random function that takes as argument a list with the form [x,y]
+    and returns 1 or -1 whether y is below the linear function defined by a*x + b or above.
+    '''
+    func = target_function(coords)
     def f(X):
         x = X[0]
         y = X[1]
@@ -48,7 +69,7 @@ def f(X):
     return f
 
 def sign(x,compare_to = 0):
-    'returns +1 or -1 by comparing x to compare_to param (by default = 0)'
+    'returns +1 or -1 by comparing (x) to (compare_to) param (by default = 0)'
     if x > compare_to:
         return +1.
     else:
@@ -84,7 +105,6 @@ def build_training_set_fmultipleparams(data,func):
         t_set.append([ [ 1.0, point[0],point[1] ] , y ])
     return t_set
 
-
 def print_avg(name,vector):
     print 'Average %s: %s'%(name,sum(vector)/(len(vector)*1.0))