tests from paper + font changed to the numexpr plot

benchmark/numexpr/gen_graph.py

@@ -150,38 +150,35 @@ def execs_timeit_2vector(exprs, fname=None):
     assert len(colors)>=len(times)
     fig = pylab.figure()
     for idx,(time,expr) in enumerate(zip(times,str_expr)):
+
+        ###
+        ###
+        ###
+        # Creating each subplot
+        ###
+        ###
+        ###
+        ###
         pylab.subplot(220+idx+1)
         pylab.subplots_adjust(wspace=0.25, hspace=0.25)
         #legend=[]
         #plot = fig.add_subplot(1,len(exprs),idx)
         speedup = [t[0].min()/t[1].min() for t in time]
+
         pylab.semilogx(nb_calls, speedup, linewidth=1.0, linestyle = '--', color='r')
         speedup = [t[0].min()/t[2].min() for t in time]
         pylab.semilogx(nb_calls, speedup, linewidth=1.0, color = 'b')
         pylab.grid(True)
         if (idx == 2) or (idx == 3):
-            pylab.xlabel('Dimension of vectors a and b')
+            pylab.xlabel('Dimension of vectors a and b', fontsize = 15)
         if (idx == 0) or (idx == 2):
-            pylab.ylabel('Speed up vs NumPy')
+            pylab.ylabel('Speed up vs NumPy', fontsize = 15)
         pylab.axhline(y=1, linewidth=1.0, color='black')
         pylab.xlim(1e3,1e7)
         pylab.xticks([1e3,1e5,1e7],['1e3','1e5','1e7'])
         pylab.title(expr)
-        #for time,expr,color in zip(times,str_expr,colors):
-        #    speedup = [t[0].min()/t[1].min() for t in time]
-        #    plot.semilogx(nb_calls, speedup, linewidth=1.0, linestyle='--', color=color)
-        #    speedup = [t[0].min()/t[2].min() for t in time]
-        #    plot.semilogx(nb_calls, speedup, linewidth=1.0, color=color)
-            #legend += ["Numexpr "+expr,"Theano "+expr]
-        
-        
-    #pylab.title('Speed up Numexpr and Theano vs NumPy')
-    #pylab.grid(True)
-    #pylab.xlabel('Nb element')
-    #pylab.ylabel('Speed up vs NumPy')
 
-    #pylab.legend(legend,loc='upper left')
-#    fig.legend(legend,loc='upper left')
+
 
     if fname:
         fig.savefig(fname)

theano/tests/diverse_tests.py

+from nose.plugins.skip import SkipTest
+
+import unittest
+import theano
+import numpy
+import random
+import numpy.random
+from theano.tests  import unittest_tools as utt
+
+'''
+  Different tests that are not connected to any particular Op, or functionality of 
+  Theano. Here will go for example code that we will publish in papers, that we 
+  should ensure that it will remain operational
+'''
+
+class T_diverse(unittest.TestCase):
+    def setUp(self):
+        utt.seed_rng()
+
+
+    def scipy_paper_example1(self):
+        a = theano.tensor.vector('a') # declare variable
+        b = a + a**10                 # build expression
+        f = theano.function([a], b)   # compile function
+        assert numpy.all(f([0,1,2]) == numpy.array([0,2,1026]))
+
+    def scipy_papaer_example2(self):
+        ''' This just sees if things compile well and if they run '''
+        x = T.matrix()
+        y = T.vector()
+        w = shared(rng.randn(100))
+        b = shared(numpy.zeros(()))
+
+        # Construct Theano expression graph
+        p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b))
+        xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1)
+        prediction = p_1 > 0.5
+        cost = xent.mean() + 0.01*(w**2).sum()
+        gw,gb = T.grad(cost, [w,b])
+
+        # Compile expressions to functions
+        train = function(
+            inputs=[x,y],
+            outputs=[prediction, xent],
+            updates={w:w-0.1*gw, b:b-0.1*gb})
+        predict = function(inputs=[x], outputs=prediction)
+
+        N = 4
+        feats = 100
+        D = (rng.randn(N, feats), rng.randint(size=4,low=0, high=2))
+        training_steps = 10
+        for i in range(training_steps):
+            pred, err = train(D[0], D[1])
+
+if __name__ == '__main__':
+    unittest.main()
+