ML - least square

Author: quangh

Machine Learning/Linear Regression/Least Square.py

@@ -0,0 +1,27 @@
+import numpy as np
+from numpy import linalg as la
+import matplotlib.pyplot as plt
+
+X = np.array([[147, 150, 153, 158, 163, 165, 168, 170, 173, 175, 178, 180, 183]]).T
+y = np.array([[49, 50, 51,  54, 58, 59, 60, 62, 63, 64, 66, 67, 68]]).T
+plt.plot(X,y,'ro')
+plt.axis([140, 190, 45, 75])
+#plt.show()
+
+one = np.ones((X.shape[0],1))
+
+X = np.hstack((one,X))
+
+#print X
+
+A = np.dot(X.T,X)
+b = np.dot(X.T,y)
+w = np.dot(la.pinv(A),b)
+
+print "w= " + str(w)
+
+x0 = np.linspace(145,185,2)
+y0 = w[0][0] + w[1][0]*x0
+
+plt.plot(x0,y0)
+plt.show()

Machine Learning/practicing numpy.py

@@ -0,0 +1,46 @@
+import numpy as np
+from numpy import linalg as la
+# a = np.arange(20).reshape(4,5)
+# print a
+# print a.ndim
+# print a.shape[0]
+# print a.size
+#
+# b = np.array([[5,6],[1,2,3]])
+# print b
+#
+# c = np.zeros((3,4))
+# print c
+#
+# d = np.ones((3,4))
+# print d
+#
+# e = np.arange(10,20,3)
+# print e
+#
+# print np.pi
+
+a = np.arange(15).reshape(3,-1)
+# print a.astype(float)
+#
+# print np.random.random((3,4))
+# print np.random.rand(3)
+print a.ravel()
+print a
+print a.T
+
+c = np.floor(10*np.random.random((2,4)))
+d = np.floor(10*np.random.random((2,4)))
+# print c
+# print d
+# print np.vstack((c,d))
+# print np.vstack((d,c))
+
+# print np.concatenate((c,d), axis=1)
+
+a = np.floor(10*np.random.random((4,4)))
+print a
+print la.inv(a)
+print np.eye(5,5)
+print np.dot(a,a)
+print np.trace(a)