``````

In [1]:

from cvxopt import matrix, solvers
Q = 2*matrix([ [2, .5], [.5, 1] ])
p = matrix([1.0, 1.0])
G = matrix([[-1.0,0.0],[0.0,-1.0]])
h = matrix([0.0,0.0])
A = matrix([1.0, 1.0], (1,2))
b = matrix(1.0)
sol=solvers.qp(Q, p, G, h, A, b)

``````
``````

pcost       dcost       gap    pres   dres
0:  1.8889e+00  7.7778e-01  1e+00  3e-16  2e+00
1:  1.8769e+00  1.8320e+00  4e-02  2e-16  6e-02
2:  1.8750e+00  1.8739e+00  1e-03  2e-16  5e-04
3:  1.8750e+00  1.8750e+00  1e-05  1e-16  5e-06
4:  1.8750e+00  1.8750e+00  1e-07  1e-16  5e-08
Optimal solution found.

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``````

In [2]:

print(sol['x'])

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``````

[ 2.50e-01]
[ 7.50e-01]

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``````

In [3]:

import rpy2

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``````

In [4]:

``````
``````

In [5]:

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

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``````

In [6]:

X = np.array([0,1,2,3,4])
Y = np.array([3,5,4,6,7])
plt.scatter(X, Y)

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``````

Out[6]:

<matplotlib.collections.PathCollection at 0x10cae5e10>

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``````

In [7]:

%Rpush X Y
%R lm(Y~X)\$coef

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``````

Out[7]:

array([ 3.2,  0.9])

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In [8]:

%R resid(lm(Y~X)); coef(lm(X~Y))

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``````

Out[8]:

array([-2.5,  0.9])

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In [9]:

b = %R a=resid(lm(Y~X))
%Rpull a
print(a)
assert id(b.data) == id(a.data)
%R -o a

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``````

[-0.2  0.9 -1.   0.1  0.2]

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``````

In [10]:

from __future__ import print_function
v1 = %R plot(X,Y); print(summary(lm(Y~X))); vv=mean(X)*mean(Y)
print('v1 is:', v1)
v2 = %R mean(X)*mean(Y)
print('v2 is:', v2)

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``````

Call:
lm(formula = Y ~ X)

Residuals:
1    2    3    4    5
-0.2  0.9 -1.0  0.1  0.2

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)   3.2000     0.6164   5.191   0.0139 *
X             0.9000     0.2517   3.576   0.0374 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7958 on 3 degrees of freedom
Multiple R-squared:   0.81,	Adjusted R-squared:  0.7467
F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739

v1 is: [ 10.]
v2 is: [ 10.]

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``````

In [11]:

%%R -i X,Y -o XYcoef
XYlm = lm(Y~X)
XYcoef = coef(XYlm)
print(summary(XYlm))
par(mfrow=c(2,2))
plot(XYlm)

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``````

Call:
lm(formula = Y ~ X)

Residuals:
1    2    3    4    5
-0.2  0.9 -1.0  0.1  0.2

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)   3.2000     0.6164   5.191   0.0139 *
X             0.9000     0.2517   3.576   0.0374 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7958 on 3 degrees of freedom
Multiple R-squared:   0.81,	Adjusted R-squared:  0.7467
F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739

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``````

In [ ]:

``````