figures

examples/activationFunPlot.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+
+# Plots various neural net activation functions.
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+
+folder = "/Users/kpmurphy/github/pyprobml/figures"
+
+e = np.exp(1)
+x = np.linspace(-4, 4, 1000)
+y = e**x / (e**x + 1)
+fig, ax = plt.subplots()
+ax.plot(x, y)
+plt.title('sigmoid function')
+plt.show()
+plt.savefig(os.path.join(folder, 'sigmoidPlot.pdf'))
+
+y = np.tanh(x)
+fig, ax = plt.subplots()
+ax.plot(x, y)
+plt.title('tanh function')
+plt.show()
+plt.savefig(os.path.join(folder, 'tanhPlot.pdf'))
+
+y = np.log(1+np.exp(x))
+fig, ax = plt.subplots()
+ax.plot(x, y)
+plt.title('softplus function')
+plt.show()
+plt.savefig(os.path.join(folder, 'softplusPlot.pdf'))
+
+
+y = 1.0*(x>0)
+fig, ax = plt.subplots()
+ax.plot(x, y)
+plt.title('Heaviside function')
+plt.show()
+plt.savefig(os.path.join(folder, 'heavisidePlot.pdf'))
+
+y = np.maximum(0, x)
+fig, ax = plt.subplots()
+ax.plot(x, y)
+plt.title('ReLU function')
+plt.show()
+plt.savefig(os.path.join(folder, 'reluPlot.pdf'))
+
+lam = 0.5
+y = np.maximum(0, x) + lam*np.minimum(0, x)
+fig, ax = plt.subplots()
+ax.plot(x, y)
+plt.title('Leaky ReLU function')
+plt.show()
+plt.savefig(os.path.join(folder, 'leakyReluPlot.pdf'))
+
+lam = 0.5
+y = np.maximum(0, x) + np.minimum(0, lam*(e**x - 1))
+fig, ax = plt.subplots()
+ax.plot(x, y)
+plt.title('ELU function')
+plt.show()
+plt.savefig(os.path.join(folder, 'eluPlot.pdf'))
+

examples/ggmLassoDemo.py

@@ -36,8 +36,8 @@
             if prec[i,j]!=0:
                 G.add_edges_from([(ProteinNames[i],ProteinNames[j])])
 
-    #ttl = 'lambda {}, nedges {}'.format(lam, len(G.edges))
-    #print(ttl)
+    ttl = 'lambda {}, nedges {}'.format(lam, len(G.edges))
+    print(ttl)
     #plt.title(ttl)
     nx.draw_circular(G, edge_color ='blue', node_color ='yellow', with_labels = True, node_size = 3000)
     plt.savefig(os.path.join('figures', 'glassoSachsPython%s.pdf' % lam))

examples/glasso.py

@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+from sklearn.covariance import GraphLasso
+import matplotlib.pyplot as plt
+import networkx as nx
+import os
+
+#These column names are from http://www-stat.stanford.edu/~tibs/ElemStatLearn/datasets/sachs.info
+ProteinNames = ['praf', 'pmek', 'plcg', 'PIP2', 'PIP3','p44/42', 'pakts473', 'PKA', 'PKC', 'P38', 'pjnk']
+NCols = len(ProteinNames)
+
+#Import the data and convert to a numpy array
+X = open(os.path.join('data', 'sachsCtsHTF.txt'), 'r').read().split()
+X = [float(x) for x in X]
+X = np.array(X).reshape(-1,NCols)
+X -= X.mean(axis=0).reshape(1,-1)
+X /= np.sqrt(1000) #same as http://www-stat.stanford.edu/~tibs/ElemStatLearn/datasets/sachs.info
+
+#Regularization parameters
+Lambs = [36, 27, 7, 0] 
+
+for lam in Lambs:
+    GL = GraphLasso(lam)
+    GL.fit(X)
+
+    prec = GL.precision_
+
+    #Form graph
+    G = nx.Graph()
+    G.add_nodes_from(ProteinNames)
+
+    for i in range(NCols):
+        for j in range(i):
+            if prec[i,j]!=0:
+                G.add_edges_from([(ProteinNames[i],ProteinNames[j])])
+
+    nx.draw_circular(G, edge_color ='blue', node_color ='yellow', with_labels = True, node_size = 3000)
+    plt.savefig(os.path.join('figures', 'glassoSachs%s.pdf' % lam))
+

examples/iris-scatterplot.py

@@ -3,6 +3,6 @@
 import os
 import seaborn as sns; sns.set(style="ticks", color_codes=True)
 iris = sns.load_dataset("iris")
-#g = sns.pairplot(iris)
+g = sns.pairplot(iris)
 g = sns.pairplot(iris, hue="species")
-plt.savefig(os.path.join('figures', 'iris-scatterplot.pdf'))
+#plt.savefig(os.path.join('figures', 'iris-scatterplot.pdf'))

figgen/daft/SRNN.py

@@ -0,0 +1,73 @@
+from matplotlib import rc
+import matplotlib.pyplot as plt
+rc("font", family="serif", size=12)
+rc("text", usetex=True)
+#rc("text.latex", preamble=open("macros.tex").read())
+#rc('text.latex',   preamble="\usepackage{amssymb}\usepackage{amsmath}\usepackage{mathrsfs}")
+
+import os
+
+import imp
+daft = imp.load_source('daft', 'daft-080308/daft.py')
+#import daft
+folder = "/Users/kpmurphy/github/pyprobml/figures"
+
+
+
+pgm = daft.PGM([3, 3], origin=[0, 0])
+
+pgm.add_node(daft.Node("x1", r"$x_{t-1}$", 1, 1))
+pgm.add_node(daft.Node("z1", r"$z_{t-1}$", 1, 2))
+pgm.add_node(daft.Node("x2", r"$x_{t}$", 2, 1))
+pgm.add_node(daft.Node("z2", r"$z_{t}$", 2, 2))
+
+pgm.add_edge("z1", "z2", linestyle="-")
+pgm.add_edge("z1", "x1", linestyle="-")
+pgm.add_edge("z2", "x2", linestyle="-")
+
+pgm.render()
+fname = "SSM-simple"
+pgm.figure.savefig(os.path.join(folder, "{}.pdf".format(fname)))
+
+
+
+
+pgm = daft.PGM([3, 3], origin=[0, 0])
+
+pgm.add_node(daft.Node("x1", r"$x_{t-1}$", 1, 1))
+pgm.add_node(daft.Node("z1", r"$h_{t-1}$", 1, 2.0,  shape="rectangle"))
+pgm.add_node(daft.Node("x2", r"$x_{t}$", 2, 1))
+pgm.add_node(daft.Node("z2", r"$h_{t}$", 2, 2.0,  shape="rectangle"))
+
+pgm.add_edge("z1", "z2", linestyle="-")
+pgm.add_edge("z1", "x1", linestyle="-")
+pgm.add_edge("z2", "x2", linestyle="-")
+pgm.add_edge("x1", "z2", linestyle="-")
+pgm.add_edge("x1", "x2", linestyle=":")
+
+pgm.render()
+fname = "RNN-simple"
+pgm.figure.savefig(os.path.join(folder, "{}.pdf".format(fname)))
+
+
+
+pgm = daft.PGM([3, 4], origin=[0, 0])
+
+pgm.add_node(daft.Node("x1", r"$x_{t-1}$", 1, 1))
+pgm.add_node(daft.Node("z1", r"$z_{t-1}$", 1, 3))
+pgm.add_node(daft.Node("h1", r"$h_{t-1}$", 1, 2.0,  shape="rectangle"))
+pgm.add_node(daft.Node("x2", r"$x_{t}$", 2, 1))
+pgm.add_node(daft.Node("z2", r"$z_{t}$", 2, 3))
+pgm.add_node(daft.Node("h2", r"$h_{t}$", 2, 2.0,  shape="rectangle"))
+
+pgm.add_edge("z1", "h1", linestyle="-")
+pgm.add_edge("h1", "h2", linestyle="-")
+pgm.add_edge("h1", "x1", linestyle="-")
+pgm.add_edge("z2", "h2", linestyle="-")
+pgm.add_edge("h2", "x2", linestyle="-")
+pgm.add_edge("x1", "h2", linestyle="-")
+pgm.add_edge("x1", "x2", linestyle=":")
+
+pgm.render()
+fname = "SRNN-simple"
+pgm.figure.savefig(os.path.join(folder, "{}.pdf".format(fname)))

daft/VRNN.py → figgen/daft/VRNN.py

@@ -3,12 +3,13 @@
 rc("font", family="serif", size=12)
 rc("text", usetex=True)
 #rc("text.latex", preamble=open("macros.tex").read())
-
+#rc('text.latex',   preamble="\usepackage{amssymb}\usepackage{amsmath}\usepackage{mathrsfs}")
+
 import os
 
 import imp
-#daft = imp.load_source('daft', 'daft-080308/daft.py')
-import daft
+daft = imp.load_source('daft', 'daft-080308/daft.py')
+#import daft
 
 
 
@@ -79,9 +80,14 @@
 pgm.add_edge("h2", "s3", linestyle="-")
 pgm.add_edge("s1", "s2", linestyle="-.")
 pgm.add_edge("s2", "s3", linestyle="-.")
-pgm.add_edge("h1", "z1", linestyle="-.")
-pgm.add_edge("h2", "z2", linestyle="-.")
-pgm.add_edge("h3", "z3", linestyle="-.")
+
+pgm.add_edge("h1", "z1", linestyle="-",)
+pgm.add_edge("h2", "z2", linestyle="-")
+pgm.add_edge("h3", "z3", linestyle="-")
+pgm.add_edge("z1", "s2", linestyle="-")
+pgm.add_edge("z2", "s3", linestyle="-")
+pgm.add_edge("z1", "h2", linestyle="-")
+pgm.add_edge("z2", "h3", linestyle="-")
 
 pgm.render()
 folder = "/Users/kpmurphy/github/pyprobml/figures"