Soft Margin Support Vector Machine
https://pythonprogramming.net/soft-margin-svm-machine-learning-tutorial/
Machine Learning – Tutorial 31
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Machine Learning – Tutorial 30
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Machine Learning – Tutorial 29
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Machine Learning – Tutorial 28
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Visualization and Predicting with our Custom SVM
https://pythonprogramming.net/predictions-svm-machine-learning-tutorial/
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
style.use('ggplot')
# build SVM class
class Support_Vector_Machine:
# The __init__ method of a class is one that runs whenever an object is created with the class
# calling self in the class allows sharing of variables across the class, so is included in all function defs
def __init__(self, visualisation=True):
# sets visualisations to what ever the user specifies (defaults to True)
self.visualisation = visualisation
# defines colours for the two states 1 & -1
self.colors = {1:'r', -1:'b'}
# sets some standards for the graphs
if self.visualisation:
self.fig = plt.figure()
self.ax = self.fig.add_subplot(1,1,1)
# train
def fit(self, data):
# set up access to the data that's passed when the function is called
self.data = data
# { ||w||: [w,b] }
opt_dict = {}
#
transforms = [[1,1],
[-1,1],
[-1,-1],
[1,-1]]
# finding values to work with for our ranges.
all_data = [] # set up a placeholder for the values
# for loop to step through data and append it to all_data (list of values)
for yi in self.data:
for featureset in self.data[yi]:
for feature in featureset:
all_data.append(feature)
# next define the max and min value in list
self.max_feature_value = max(all_data)
self.min_feature_value = min(all_data)
# free up memory once we've got the values
all_data=None
# define step size for optimisation Big through to small
step_sizes = [self.max_feature_value * 0.1,
self.max_feature_value * 0.01,
# starts getting very high cost after this.
self.max_feature_value * 0.001]
# extremely expensive
b_range_multiple = 5
b_multiple = 5
# first element in vector w
latest_optimum = self.max_feature_value*10
## Begin the stepping process
for step in step_sizes:
w = np.array([latest_optimum,latest_optimum])
# we can do this because convex
optimized = False
while not optimized:
# we're not optimising b as much as w (not needed)
for b in np.arange(-1*(self.max_feature_value*b_range_multiple),
self.max_feature_value*b_range_multiple,
step*b_multiple):
for transformation in transforms:
w_t = w*transformation
found_option = True
# weakest link in the SVM fundamentally
# SMO attempts to fix this a bit
# yi(xi.w+b) >= 1
#
# #### add a break here later..
for i in self.data:
for xi in self.data[i]:
yi=i
if not yi*(np.dot(w_t,xi)+b) >= 1:
found_option = False
if found_option:
opt_dict[np.linalg.norm(w_t)] = [w_t,b]
if w[0]<0:
optimized = True
print('optimised a step')
else:
w = w - step
# break out of while loop
# take a list of the magnitudes and sort them
norms = sorted([n for n in opt_dict]) # sorting lowest to highest
#||w|| : [w,b]
opt_choice = opt_dict[norms[0]] # smallest magnitude
self.w = opt_choice[0] # sets w to first element in the smallest mag
self.b = opt_choice[1] # sets b to second element in the smallest mag
latest_optimum = opt_choice[0][0]+step*2 # resetting the opt to the latest
def predict(self,features):
# sign( x.w+b )
classification = np.sign(np.dot(np.array(features),self.w)+self.b)
if classification !=0 and self.visualisation:
self.ax.scatter(features[0], features[1], s=100, marker='*', c=self.colors[classification])
return classification
def visualise(self):
#scattering known featuresets using a one line for loop
[[self.ax.scatter(x[0],x[1],s=100,color=self.colors[i]) for x in data_dict[i]] for i in data_dict]
# hyperplane = x.w+b
def hyperplane(x,w,b,v):
# v = (w.x+b)
return (-w[0]*x-b+v) / w[1]
datarange = (self.min_feature_value*0.9,self.max_feature_value*1.1) # gives space on the graph
hyp_x_min = datarange[0]
hyp_x_max = datarange[1]
# w.x + b = 1
# pos sv hyperplane
psv1 = hyperplane(hyp_x_min, self.w, self.b, 1) # define the ys
psv2 = hyperplane(hyp_x_max, self.w, self.b, 1) # define the ys
self.ax.plot([hyp_x_min,hyp_x_max], [psv1,psv2], "k") # plot xs, ys then colour k=black g-- = green
# w.x + b = -1
# negative sv hyperplane
nsv1 = hyperplane(hyp_x_min, self.w, self.b, -1)
nsv2 = hyperplane(hyp_x_max, self.w, self.b, -1)
self.ax.plot([hyp_x_min,hyp_x_max], [nsv1,nsv2], "k")
# w.x + b = 0
# decision
db1 = hyperplane(hyp_x_min, self.w, self.b, 0)
db2 = hyperplane(hyp_x_max, self.w, self.b, 0)
self.ax.plot([hyp_x_min,hyp_x_max], [db1,db2], "g--")
plt.show()
# define data dictionary
data_dict = {-1:np.array([[1,7],
[2,8],
[3,8],]),
1:np.array([[5,1],
[6,-1],
[7,3],])}
svm = Support_Vector_Machine()
svm.fit(data=data_dict)
predict_us = [[0,10],
[1,3],
[3,4],
[3,5],
[5,5],
[5,6],
[6,-5],
[5,8]]
for p in predict_us:
svm.predict(p)
svm.visualise()
Machine Learning – Tutorial 27
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Support Vector Machine Optimization in Python part 2
https://pythonprogramming.net/svm-optimization-python-2-machine-learning-tutorial/
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
style.use('ggplot')
# build SVM class
class Support_Vector_Machine:
# The __init__ method of a class is one that runs whenever an object is created with the class
# calling self in the class allows sharing of variables across the class, so is included in all function defs
def __init__(self, visualisation=True):
# sets visualisations to what ever the user specifies (defaults to True)
self.visualisation = visualisation
# defines colours for the two states 1 & -1
self.colors = {1:'r', -1:'b'}
# sets some standards for the graphs
if self.visualisation:
self.fig = plt.figure()
self.ax = self.fig.add_subplot(1,1,1)
# train
def fit(self, data):
# set up access to the data that's passed when the function is called
self.data = data
# { ||w||: [w,b] }
opt_dict = {}
#
transforms = [[1,1],
[-1,1],
[-1,-1],
[1,-1]]
# finding values to work with for our ranges.
all_data = [] # set up a placeholder for the values
# for loop to step through data and append it to all_data (list of values)
for yi in self.data:
for featureset in self.data[yi]:
for feature in featureset:
all_data.append(feature)
# next define the max and min value in list
self.max_feature_value = max(all_data)
self.min_feature_value = min(all_data)
# free up memory once we've got the values
all_data=None
# define step size for optimisation Big through to small
step_sizes = [self.max_feature_value * 0.1,
self.max_feature_value * 0.01,
# starts getting very high cost after this.
self.max_feature_value * 0.001]
# extremely expensive
b_range_multiple = 5
b_multiple = 5
# first element in vector w
latest_optimum = self.max_feature_value*10
## Begin the stepping process
for step in step_sizes:
w = np.array([latest_optimum,latest_optimum])
# we can do this because convex
optimized = False
while not optimized:
# we're not optimising b as much as w (not needed)
for b in np.arange(-1*(self.max_feature_value*b_range_multiple),
self.max_feature_value*b_range_multiple,
step*b_multiple):
for transformation in transforms:
w_t = w*transformation
found_option = True
# weakest link in the SVM fundamentally
# SMO attempts to fix this a bit
# yi(xi.w+b) >= 1
#
# #### add a break here later..
for i in self.data:
for xi in self.data[i]:
yi=i
if not yi*(np.dot(w_t,xi)+b) >= 1:
found_option = False
if found_option:
opt_dict[np.linalg.norm(w_t)] = [w_t,b]
if w[0]<0:
optimized = True
print('optimised a step')
else:
w = w - step
# break out of while loop
# take a list of the magnitudes and sort them
norms = sorted([n for n in opt_dict]) # sorting lowest to highest
#||w|| : [w,b]
opt_choice = opt_dict[norms[0]] # smallest magnitude
self.w = opt_choice[0] # sets w to first element in the smallest mag
self.b = opt_choice[1] # sets b to second element in the smallest mag
latest_optimum = opt_choice[0][0]+step*2 # resetting the opt to the latest
def predict(self,features):
# sign( x.w+b )
classification = np.sign(np.dot(np.array(features),self.w)+self.b)
return classification
# define data dictionary
data_dict = {-1:np.array([[1,7],
[2,8],
[3,8],]),
1:np.array([[5,1],
[6,-1],
[7,3],])}
Machine Learning – Tutorial 26
galiquis
Support Vector Machine Optimization in Python
https://pythonprogramming.net/svm-optimization-python-machine-learning-tutorial/
More resources:-
- Convex Optimization Book: https://web.stanford.edu/~boyd/cvxboo…
- equential Minimal Optimization book: http://research.microsoft.com/pubs/68…
- More SMO: http://research.microsoft.com/pubs/69…
- CVXOPT (Convex Optimization Module for Python): http://cvxopt.org/
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
style.use('ggplot')
# build SVM class
class Support_Vector_Machine:
# The __init__ method of a class is one that runs whenever an object is created with the class
# calling self in the class allows sharing of variables across the class, so is included in all function defs
def __init__(self, visualisation=True):
# sets visualisations to what ever the user specifies (defaults to True)
self.visualisation = visualisation
# defines colours for the two states 1 & -1
self.colors = {1:'r', -1:'b'}
# sets some standards for the graphs
if self.visualisation:
self.fig = plt.figure()
self.ax = self.fig.add_subplot(1,1,1)
# train
def fit(self, data):
# set up access to the data that's passed when the function is called
self.data = data
# { ||w||: [w,b] }
opt_dict = {}
#
transforms = [[1,1],
[-1,1],
[-1,-1],
[1,-1]]
# finding values to work with for our ranges.
all_data = [] # set up a placeholder for the values
# for loop to step through data and append it to all_data (list of values)
for yi in self.data:
for featureset in self.data[yi]:
for feature in featureset:
all_data.append(feature)
# next define the max and min value in list
self.max_feature_value = max(all_data)
self.min_feature_value = min(all_data)
# free up memory once we've got the values
all_data=None
# define step size for optimisation Big through to small
step_sizes = [self.max_feature_value * 0.1,
self.max_feature_value * 0.01,
# starts getting very high cost after this.
self.max_feature_value * 0.001]
# extremely expensive
b_range_multiple = 5
b_multiple = 5
# first element in vector w
latest_optimum = self.max_feature_value*10
## Begin the stepping process
for step in step_sizes:
w = np.array([latest_optimum,latest_optimum])
# we can do this because convex
optimized = False
while not optimized:
pass
def predict(self,features):
# sign( x.w+b )
classification = np.sign(np.dot(np.array(features),self.w)+self.b)
return classification
# define data dictionary
data_dict = {-1:np.array([[1,7],
[2,8],
[3,8],]),
1:np.array([[5,1],
[6,-1],
[7,3],])}
Machine Learning – Tutorial 25
galiquis
Beginning SVM from Scratch in Python
https://pythonprogramming.net/svm-in-python-machine-learning-tutorial/
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
style.use('ggplot')
# build SVM class
class Support_Vector_Machine:
# The __init__ method of a class is one that runs whenever an object is created with the class
# calling self in the class allows sharing of variables across the class, so is included in all function defs
def __init__(self, visualisation=True):
# sets visualisations to what ever the user specifies (defaults to True)
self.visualisation = visualisation
# defines colours for the two states 1 & -1
self.colors = {1:'r', -1:'b'}
# sets some standards for the graphs
if self.visualisation:
self.fig = plt.figure()
self.ax = self.fig.add_subplot(1,1,1)
# train
def fit(self, data):
pass
def predict(self,features):
# sign( x.w+b )
classification = np.sign(np.dot(np.array(features),self.w)+self.b)
return classification
# define data dictionary
data_dict = {-1:np.array([[1,7],
[2,8],
[3,8],]),
1:np.array([[5,1],
[6,-1],
[7,3],])}
Machine Learning – Tutorial 24
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Constraint Optimization with Support Vector Machine
https://pythonprogramming.net/svm-constraint-optimization-machine-learning-tutorial/
Machine Learning – Tutorial 23
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Support Vector Machine Fundamentals
https://pythonprogramming.net/support-vector-machine-fundamentals-machine-learning-tutorial/
Machine Learning – Tutorial 22
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Support Vector Assertions
https://pythonprogramming.net/support-vector-assertions-machine-learning-tutorial/