Support Vector Machines

import numpy as np

from sklearn.datasets import make$\_$blobs

from matplotlib.colors import Normalize

import matplotlib.pyplot as plt

def linear$\_$plot$($X$,$ y$,$ w$=$None, b$=$None$)$:

mycolors $=\{"$blue$"$: "steelblue", "red": $"$#a$76$c$6$e$",$ "green": $"$#$6$a$9373"\}$

colors $=[$mycolors$["$red$"]$ if yi$==1$ else mycolors$["$blue$"]$ for yi in y$]$

# Plot data

fig, ax $=$ plt$.$subplots$($nrows$=1,$ ncols$=1,$ figsize$=(8,8))$

ax$.$scatter$($X$[$:$,0],$ X$[$:$,1],$ color$=$colors, s$=150,$ alpha$=0\mathrm{.}95,$ zorder$=2)$

# Plot boundaries

lower$\_$left $=$ np$.$min$([$np$.$min$($X$[$:$,0]),$ np$.$min$($X$[$:$,1])])$

upper$\_$right $=$ np$.$max$([$np$.$max$($X$[$:$,0]),$ np$.$max$($X$[$:$,1])])$

gap $=\mathrm{.}1*($upper$\_$right$-$lower$\_$left$)$

xplot $=$ np$.$linspace$($lower$\_$left$-$gap, upper$\_$right$+$gap, $20)$

if w is not None and b is not None:

ax$.$plot$($xplot$,(-$b $-$ w$[0]*$xplot$)/$w$[1],$ color$=$"gray", lw$=2,$ zorder$=1)$

ax$.$plot$($xplot$,(1-$b $-$ w$[0]*$xplot$)/$w$[1],$ color$=$"gray", lw$=2,$ ls$="--",$ zorder$=1)$

ax$.$plot$($xplot$,(-1-$b $-$ w$[0]*$xplot$)/$w$[1],$ color$=$"gray", lw$=2,$ ls$="--",$ zorder$=1)$

ax$.$set$\_$xlim$([$lower$\_$left$-$gap, upper$\_$right$+$gap$])$

ax$.$set$\_$ylim$([$lower$\_$left$-$gap, upper$\_$right$+$gap$])$

ax$.$grid$($alpha$=0\mathrm{.}25)$

def part$2$data$()$:

np$.$random.seed$(1239)$

X $=$ np$.$zeros$((22,2))$

X$[0$:$10,0]=1\mathrm{.}5*$np$.$random.rand$(10)$

X$[0$:$10,1]=1\mathrm{.}5*$np$.$random.rand$(10)$

X$[10$:$20,0]=1\mathrm{.}5*$np$.$random.rand$(10)+1\mathrm{.}75$

X$[10$:$20,1]=1\mathrm{.}5*$np$.$random.rand$(10)+1$

X$[20,0]=1\mathrm{.}5$

X$[20,1]=2\mathrm{.}25$

X$[21,0]=1\mathrm{.}6$

X$[21,1]=0\mathrm{.}25$

y $=$ np$.$ones$(22)$

y$[10$:$20]=-1$

y$[20]=1$

y$[21]=-1$

return X$,$ y

def part$3$data$($N$=100,$ seed$=1235)$:

np$.$random.seed$($seed$)$

X $=$ np$.$random.uniform$(-1,1,($N$,2))$

y $=$ np$.$array$([1$ if y$-$x $>0$ else $-1$ for $($x$,$y$)$ in zip$($X$[$:$,0]**2*$ np$.$sin$(2*$np$.$pi$*$X$[$:$,0]),$ X$[$:$,1])])$

X $=$ X $+$ np$.$random.normal$(0,\mathrm{.}1,($N$,2))$

return X$,$ y

def nonlinear$\_$plot$($X$,$ y$,$ clf$=$None$)$:

mycolors $=\{"$blue$"$: "steelblue", "red": $"$#a$76$c$6$e$",$ "green": $"$#$6$a$9373"\}$

fig, ax $=$ plt$.$subplots$($nrows$=1,$ ncols$=1,$ figsize$=(10,10))$

colors $=[$mycolors$["$red$"]$ if yi$==1$ else mycolors$["$blue$"]$ for yi in y$]$

ax$.$scatter$($X$[$:$,0],$X$[$:$,1],$ marker$=\text{'}$o$\text{'},$ color$=$colors, s$=100,$ alpha$=0\mathrm{.}5)$

ax$.$arrow$(-1\mathrm{.}25,0,2\mathrm{.}5,0,$ head$\_$length$=0\mathrm{.}05,$ head$\_$width$=0\mathrm{.}05,$ fc$=$"gray", ec$=$"gray", lw$=2,$ alpha$=0\mathrm{.}25)$

ax$.$arrow$(0,-1\mathrm{.}25,0,2\mathrm{.}5,$ head$\_$length$=0\mathrm{.}05,$ head$\_$width$=0\mathrm{.}05,$ fc$=$"gray", ec$=$"gray", lw$=2,$ alpha$=0\mathrm{.}25)$

z $=$ np$.$linspace$(0\mathrm{.}25,3\mathrm{.}5,10)$

ax$.$set$\_$xlim$([-1\mathrm{.}50,1\mathrm{.}50])$

ax$.$set$\_$ylim$([-1\mathrm{.}50,1\mathrm{.}50])$

ax$.$spines$[\text{'}$top$\text{'}].$set$\_$visible$($False$)$

ax$.$spines$[\text{'}$right$\text{'}].$set$\_$visible$($False$)$

ax$.$spines$[\text{'}$bottom$\text{'}].$set$\_$visible$($False$)$

ax$.$spines$[\text{'}$left$\text{'}].$set$\_$visible$($False$)$

plt$.$xticks$([],$ fontsize$=16)$

plt$.$yticks$([],$ fontsize$=16)$

if clf:

clf$.$fit$($X$,$y$)$

x$\_$min $=$ X$[$:$,0].$min$()+\mathrm{.}00$

x$\_$max $=$ X$[$:$,0].$max$()-\mathrm{.}00$

y$\_$min $=$ X$[$:$,1].$min$()+\mathrm{.}00$

y$\_$max $=$ X$[$:$,1].$max$()-\mathrm{.}00$

colors $=[$mycolors$["$red$"]$ if yi$==1$ else mycolors$["$blue$"]$ for yi in y$]$

XX$,$ YY $=$ np$.$mgrid$[$x$\_$min:x$\_$max:$200$j$,$ y$\_$min:y$\_$max:$200$j$]$

Z $=$ clf$.$decision$\_$function$($np$.$c$\_[$XX$.$ravel$(),$ YY$.$ravel$()])$

# Put the result into a color plot

Z $=$ Z$.$reshape$($XX$.$shape$)$

plt$.$contour$($XX$,$ YY$,$ Z$,$ colors$=[$mycolors$["$blue$"],$ "gray", mycolors$["$red$"]],$ linestyles$=[\text{'}--\text{'},\text{'}-\text{'},\text{'}--\text{'}],$

levels$=[-1\mathrm{.}0,0,1\mathrm{.}0],$ linewidths$=[2,2,2],$ alpha$=0\mathrm{.}9)$

class MidpointNormalize$($Normalize$)$:

def $\_\_$init$\_\_($self$,$ vmin$=$None, vmax$=$None, midpoint$=$None, clip$=$False$)$:

self.midpoint $=$ midpoint

Normalize.$\_\_$init$\_\_($self$,$ vmin, vmax, clip$)$

def $\_\_$call$\_\_($self$,$ value, clip$=$None$)$:

x$,$ y $=[$self$.$vmin, self.midpoint, self.vmax$],[0,0\mathrm{.}5,1]$

return np$.$ma$.$masked$\_$array$($np$.$interp$($value$,$ x$,$ y$))$

def plotSearchGrid$($grid$)$:

scores $=[$x for x in grid.cv$\_$results$\_["$mean$\_$test$\_$score"$]]$

scores $=$ np$.$array$($scores$).$reshape$($len$($grid$.$param$\_$grid$["$C$"]),$ len$($grid$.$param$\_$grid$["$gamma$"]))$

plt$.$figur

Step by Step Solution

There are 3 Steps involved in it

1 Expert Approved Answer

Step: 1 Unlock

Question Has Been Solved by an Expert!

Get step-by-step solutions from verified subject matter experts

Step: 2 Unlock

Step: 3 Unlock

Students Have Also Explored These Related Programming Questions!

Q:

Solve all parts with code The google colab code/file is : { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression for Red Wine Quality Classification" ] }, {...

Q:

CAN YOU SOLVE BOTH PARTS WITH ACTUAL CODE IN GOOGLE COLAB USING THE . ipynb file copied and pasted below! { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression for...

Q:

Using the Scikit-Learn Dataset To load the sample scikit data set, import the datasets module and load the desired dataset. Code Run: from sklearn import datasets import pandas as pd diabetes =...

Q:

[PYTHON] In this problem we'll figure out how to fit linear SVM models to data using sklearn. Consider the data shown below. import numpy as np from sklearn.datasets import make_blobs from...

Q:

Python language please. Thank you! #%% md # Setup First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that...

Q:

Expert please complete this correctly scipy.integrate.quad scipy.integrate. quad (func, a, b, args=(), full_output=0, epsabs=1.49e-08, epsrel=1.49e-08, limit=50, wvar=None, wopts=None, maxp1=50,...

Q:

Python language please. Thank you! #%% md # Setup First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that...

Q:

Python Code as below Can you please help plotting Plot_Data_Elem_processed import numpy as np import matplotlib.pyplot as plt from matplotlib import ticker, rcParams import sys import os def...

Q:

from sklearn.datasets import load _ iris iris = load _ iris ( ) import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans feature = iris . data X = feature [ : , [ 2 , 3 ]...

Q:

Jupyter NoteBook Once we decide to measure more than three features per input vector, it can become challenging to understand how a network is learning to solve such a problem since we can no longer...

Q:

Show that the sound intensity level can also be expressed as P B = 10 log P (Show your step-by-step solution.) (3 pts)

Q:

1. What is total quality management? How is it relevant for automobiles? 2. What is the difference between internal and external benchmarking? Do the J.D. Power Initial Quality and Vehicle...

Q:

A city wants to issue municipal debt to pay for a stadium. This debt would mostly be issued as a a . Tax Exempt Revenue Bond b . Tax Exempt Tax - backed Bond c . Taxable Revenue Bond d . Taxable Tax...

Q:

Given p x 4x 1 and q x x 3 find each p q x q p 0 ven p(x) = 4x 1 and q(x) = x + 3, find eadl.

Previous Question Next Question