import advancedclassify
ageonly = advancedclassify.loadmatch('agesonly.csv', allnum = True)
advancedclassify.plotagematches(ageonly)
def test_pg_197_to_214():
"""
Matchmaker Dataset
"""
import advancedclassify
agesonly = advancedclassify.loadmatch("agesonly.csv", allnum=True)
matchmaker = advancedclassify.loadmatch("matchmaker.csv")
"""
Difficulties with the data
"""
# Scatter plot of mans age vs womans age
# O is a match
# X is not a match
advancedclassify.plotagematches(agesonly)
"""
Basic linear classification
"""
avgs = advancedclassify.lineartrain(agesonly)
print advancedclassify.dpclassify([30,30], avgs)
print advancedclassify.dpclassify([30,25], avgs)
print advancedclassify.dpclassify([25,40], avgs)
print advancedclassify.dpclassify([48,20], avgs)
"""
Determing distances using Yahoo! maps
"""
print advancedclassify.milesdistance("cambride, ma", "new york, ny")
"""
Creating the new dataset
"""
numericalset = advancedclassify.loadnumerical()
print numericalset[0].data
"""
Scaling the dataset.
"""
scaledset, scalef = advancedclassify.scaledata(numericalset)
avgs = advancedclassify.lineartrain(scaledset)
print numericalset[0].data
print numericalset[0].match
print advancedclassify.dpclassify(scalef(numericalset[0].data), avgs)
print numericalset[11].match
print advancedclassify.dpclassify(scalef(numericalset[11].data), avgs)
"""
The kernel trick
"""
offset = advancedclassify.getoffset(agesonly)
print offset
print advancedclassify.nlclassify([30, 30], agesonly, offset)
print advancedclassify.nlclassify([30, 25], agesonly, offset)
print advancedclassify.nlclassify([25, 40], agesonly, offset)
# In contrast to linear classification now recognises that
# 48, 20 is not a good match
print advancedclassify.nlclassify([48, 20], agesonly, offset)
ssoffset = advancedclassify.getoffset(scaledset)
# 0
print numericalset[0].match
# 0
print advancedclassify.nlclassify(scalef(numericalset[0].data), scaledset, ssoffset)
# 1
print numericalset[1].match
# 1
print advancedclassify.nlclassify(scalef(numericalset[1].data), scaledset, ssoffset)
# 0
print numericalset[2].match
# 0
# print advancedclassify.nlclassify(scalef(numericalset[2].data), scaledset, ssoffset)
# man doesnt want children, women does, otherwise really gd match
newrow=[28.0, -1, -1, 26.0, -1, 1, 2, 0.8]
# 0
print advancedclassify.nlclassify(scalef(numericalset[0].data), scaledset, ssoffset)
# both want children
newrow=[28.0, -1, 1, 26.0, -1, 1, 2, 0.8]
# 0
print advancedclassify.nlclassify(scalef(numericalset[0].data), scaledset, ssoffset)
def dataPlot(): print '## Difficulties with the Data' reload(advancedclassify) advancedclassify.plotagematches(agesonly)
import advancedclassify
ageonly = advancedclassify.loadmatch('agesonly.csv', allnum=True)
advancedclassify.plotagematches(ageonly)
def visualize(agesonly):
print "visualizing..."
advancedclassify.plotagematches(agesonly)
def test_pg_197_to_214():
"""
Matchmaker Dataset
"""
import advancedclassify
agesonly = advancedclassify.loadmatch("agesonly.csv", allnum=True)
matchmaker = advancedclassify.loadmatch("matchmaker.csv")
"""
Difficulties with the data
"""
# Scatter plot of mans age vs womans age
# O is a match
# X is not a match
advancedclassify.plotagematches(agesonly)
"""
Basic linear classification
"""
avgs = advancedclassify.lineartrain(agesonly)
print advancedclassify.dpclassify([30, 30], avgs)
print advancedclassify.dpclassify([30, 25], avgs)
print advancedclassify.dpclassify([25, 40], avgs)
print advancedclassify.dpclassify([48, 20], avgs)
"""
Determing distances using Yahoo! maps
"""
print advancedclassify.milesdistance("cambride, ma", "new york, ny")
"""
Creating the new dataset
"""
numericalset = advancedclassify.loadnumerical()
print numericalset[0].data
"""
Scaling the dataset.
"""
scaledset, scalef = advancedclassify.scaledata(numericalset)
avgs = advancedclassify.lineartrain(scaledset)
print numericalset[0].data
print numericalset[0].match
print advancedclassify.dpclassify(scalef(numericalset[0].data), avgs)
print numericalset[11].match
print advancedclassify.dpclassify(scalef(numericalset[11].data), avgs)
"""
The kernel trick
"""
offset = advancedclassify.getoffset(agesonly)
print offset
print advancedclassify.nlclassify([30, 30], agesonly, offset)
print advancedclassify.nlclassify([30, 25], agesonly, offset)
print advancedclassify.nlclassify([25, 40], agesonly, offset)
# In contrast to linear classification now recognises that
# 48, 20 is not a good match
print advancedclassify.nlclassify([48, 20], agesonly, offset)
ssoffset = advancedclassify.getoffset(scaledset)
# 0
print numericalset[0].match
# 0
print advancedclassify.nlclassify(scalef(numericalset[0].data), scaledset,
ssoffset)
# 1
print numericalset[1].match
# 1
print advancedclassify.nlclassify(scalef(numericalset[1].data), scaledset,
ssoffset)
# 0
print numericalset[2].match
# 0
# print advancedclassify.nlclassify(scalef(numericalset[2].data), scaledset, ssoffset)
# man doesnt want children, women does, otherwise really gd match
newrow = [28.0, -1, -1, 26.0, -1, 1, 2, 0.8]
# 0
print advancedclassify.nlclassify(scalef(numericalset[0].data), scaledset,
ssoffset)
# both want children
newrow = [28.0, -1, 1, 26.0, -1, 1, 2, 0.8]
# 0
print advancedclassify.nlclassify(scalef(numericalset[0].data), scaledset,
ssoffset)
