how to use SVM to match the testing data having one in result? [on hold]

Clash Royale CLAN TAG#URR8PPP

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why svm result do not have one in result?

how to calculate result having one which is correct with testing data?

Description:
i use eigenvalues as features and label 1 when match future value,
and label 0 for the rest not match,

training 20 rows data and testing 20 rows data

if rising within range 10 dollars and dropping within range 10 dollars considered, each increment is 0.1 dollar, so 200 training data for one day

from numpy import linalg as LA
import numpy as np
import matplotlib.pyplot as plt

import sys
import os
import pandas as pd
import datetime as dt
import re
import numpy as np
from dateutil import parser
import datetime

from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.externals import joblib
from sklearn import metrics
from sklearn.model_selection import learning_curve
from sklearn.svm import SVC
from sklearn.model_selection import validation_curve

currentdirectory = os.getcwd()
data = pd.read_excel(os.path.join(currentdirectory,"0005.HK2017.xls"), '0005.HK')
df = pd.DataFrame()
df = df.append(data)

ii = 1
jj = df['Close'][1:].tolist()
n = 0
y_train = 
x_train = 
testing = 20
for i in range(1+testing, 10+testing):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_train.append(list(w))
 if jj[i+0]+j*0.1 == jj[i-1]:
 y_train.append(1)
 else:
 y_train.append(0)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_train.append(list(w))
 if jj[i+0]-j*0.1 == jj[i-1]:
 y_train.append(1)
 else:
 y_train.append(0)

#clf = svm.SVC(kernel='linear')
clf = svm.SVC()
clf.fit(x_train, y_train)

y_test = 
x_test = 
testing = 0
for i in range(1+testing, 10+testing):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_test.append(list(w))
 if jj[i+0]+j*0.1 == jj[i-1]:
 y_test.append(1)
 else:
 y_test.append(0)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_test.append(list(w))
 if jj[i+0]-j*0.1 == jj[i-1]:
 y_test.append(1)
 else:
 y_test.append(0)

result = clf.predict(np.asarray(x_test))
metrics.f1_score(y_test, result, average='macro')

np.savetxt("runningresult.csv", result, delimiter=",")
np.savetxt("y_test.csv", y_test, delimiter=",")

There should be at least one one in result if it match

for i in range(1, 2):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 if clf.predict([w]) == 1:
 print(jj[i+0]+j*0.1)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 if clf.predict([w]) == 1:
 print(jj[i+0]+j*0.1)

machine-learning

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edited 5 hours ago

Khovanov

105

asked 14 hours ago

HYL

121

put on hold as off-topic by amWhy, Siong Thye Goh, Arnaud Mortier, max_zorn, José Carlos Santos 13 hours ago

This question appears to be off-topic. The users who voted to close gave this specific reason:

• "This question is not about mathematics, within the scope defined in the help center." – amWhy, Siong Thye Goh, Arnaud Mortier, max_zorn, José Carlos Santos

If this question can be reworded to fit the rules in the help center, please edit the question.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  Do you really think that anybody can understand your code, which is without any comment? Moreover, are you sure that this question is about mathematics?
  – Taroccoesbrocco
  13 hours ago
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  i add description
  – HYL
  13 hours ago
  

  
  
  
  

add a comment | 

up vote
-1
down vote

favorite

why svm result do not have one in result?

how to calculate result having one which is correct with testing data?

Description:
i use eigenvalues as features and label 1 when match future value,
and label 0 for the rest not match,

training 20 rows data and testing 20 rows data

if rising within range 10 dollars and dropping within range 10 dollars considered, each increment is 0.1 dollar, so 200 training data for one day

from numpy import linalg as LA
import numpy as np
import matplotlib.pyplot as plt

import sys
import os
import pandas as pd
import datetime as dt
import re
import numpy as np
from dateutil import parser
import datetime

from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.externals import joblib
from sklearn import metrics
from sklearn.model_selection import learning_curve
from sklearn.svm import SVC
from sklearn.model_selection import validation_curve

currentdirectory = os.getcwd()
data = pd.read_excel(os.path.join(currentdirectory,"0005.HK2017.xls"), '0005.HK')
df = pd.DataFrame()
df = df.append(data)

ii = 1
jj = df['Close'][1:].tolist()
n = 0
y_train = 
x_train = 
testing = 20
for i in range(1+testing, 10+testing):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_train.append(list(w))
 if jj[i+0]+j*0.1 == jj[i-1]:
 y_train.append(1)
 else:
 y_train.append(0)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_train.append(list(w))
 if jj[i+0]-j*0.1 == jj[i-1]:
 y_train.append(1)
 else:
 y_train.append(0)

#clf = svm.SVC(kernel='linear')
clf = svm.SVC()
clf.fit(x_train, y_train)

y_test = 
x_test = 
testing = 0
for i in range(1+testing, 10+testing):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_test.append(list(w))
 if jj[i+0]+j*0.1 == jj[i-1]:
 y_test.append(1)
 else:
 y_test.append(0)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_test.append(list(w))
 if jj[i+0]-j*0.1 == jj[i-1]:
 y_test.append(1)
 else:
 y_test.append(0)

result = clf.predict(np.asarray(x_test))
metrics.f1_score(y_test, result, average='macro')

np.savetxt("runningresult.csv", result, delimiter=",")
np.savetxt("y_test.csv", y_test, delimiter=",")

There should be at least one one in result if it match

for i in range(1, 2):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 if clf.predict([w]) == 1:
 print(jj[i+0]+j*0.1)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 if clf.predict([w]) == 1:
 print(jj[i+0]+j*0.1)

machine-learning

share|cite|improve this question

edited 5 hours ago

Khovanov

105

asked 14 hours ago

HYL

121

put on hold as off-topic by amWhy, Siong Thye Goh, Arnaud Mortier, max_zorn, José Carlos Santos 13 hours ago

This question appears to be off-topic. The users who voted to close gave this specific reason:

• "This question is not about mathematics, within the scope defined in the help center." – amWhy, Siong Thye Goh, Arnaud Mortier, max_zorn, José Carlos Santos

If this question can be reworded to fit the rules in the help center, please edit the question.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  Do you really think that anybody can understand your code, which is without any comment? Moreover, are you sure that this question is about mathematics?
  – Taroccoesbrocco
  13 hours ago
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  i add description
  – HYL
  13 hours ago
  

  
  
  
  

add a comment | 

up vote
-1
down vote

favorite

up vote
-1
down vote

favorite

why svm result do not have one in result?

how to calculate result having one which is correct with testing data?

Description:
i use eigenvalues as features and label 1 when match future value,
and label 0 for the rest not match,

training 20 rows data and testing 20 rows data

if rising within range 10 dollars and dropping within range 10 dollars considered, each increment is 0.1 dollar, so 200 training data for one day

from numpy import linalg as LA
import numpy as np
import matplotlib.pyplot as plt

import sys
import os
import pandas as pd
import datetime as dt
import re
import numpy as np
from dateutil import parser
import datetime

from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.externals import joblib
from sklearn import metrics
from sklearn.model_selection import learning_curve
from sklearn.svm import SVC
from sklearn.model_selection import validation_curve

currentdirectory = os.getcwd()
data = pd.read_excel(os.path.join(currentdirectory,"0005.HK2017.xls"), '0005.HK')
df = pd.DataFrame()
df = df.append(data)

ii = 1
jj = df['Close'][1:].tolist()
n = 0
y_train = 
x_train = 
testing = 20
for i in range(1+testing, 10+testing):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_train.append(list(w))
 if jj[i+0]+j*0.1 == jj[i-1]:
 y_train.append(1)
 else:
 y_train.append(0)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_train.append(list(w))
 if jj[i+0]-j*0.1 == jj[i-1]:
 y_train.append(1)
 else:
 y_train.append(0)

#clf = svm.SVC(kernel='linear')
clf = svm.SVC()
clf.fit(x_train, y_train)

y_test = 
x_test = 
testing = 0
for i in range(1+testing, 10+testing):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_test.append(list(w))
 if jj[i+0]+j*0.1 == jj[i-1]:
 y_test.append(1)
 else:
 y_test.append(0)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_test.append(list(w))
 if jj[i+0]-j*0.1 == jj[i-1]:
 y_test.append(1)
 else:
 y_test.append(0)

result = clf.predict(np.asarray(x_test))
metrics.f1_score(y_test, result, average='macro')

np.savetxt("runningresult.csv", result, delimiter=",")
np.savetxt("y_test.csv", y_test, delimiter=",")

There should be at least one one in result if it match

for i in range(1, 2):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 if clf.predict([w]) == 1:
 print(jj[i+0]+j*0.1)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 if clf.predict([w]) == 1:
 print(jj[i+0]+j*0.1)

machine-learning

share|cite|improve this question

edited 5 hours ago

Khovanov

105

asked 14 hours ago

HYL

121

why svm result do not have one in result?

how to calculate result having one which is correct with testing data?

Description:
i use eigenvalues as features and label 1 when match future value,
and label 0 for the rest not match,

training 20 rows data and testing 20 rows data

if rising within range 10 dollars and dropping within range 10 dollars considered, each increment is 0.1 dollar, so 200 training data for one day

from numpy import linalg as LA
import numpy as np
import matplotlib.pyplot as plt

import sys
import os
import pandas as pd
import datetime as dt
import re
import numpy as np
from dateutil import parser
import datetime

from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.externals import joblib
from sklearn import metrics
from sklearn.model_selection import learning_curve
from sklearn.svm import SVC
from sklearn.model_selection import validation_curve

currentdirectory = os.getcwd()
data = pd.read_excel(os.path.join(currentdirectory,"0005.HK2017.xls"), '0005.HK')
df = pd.DataFrame()
df = df.append(data)

ii = 1
jj = df['Close'][1:].tolist()
n = 0
y_train = 
x_train = 
testing = 20
for i in range(1+testing, 10+testing):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_train.append(list(w))
 if jj[i+0]+j*0.1 == jj[i-1]:
 y_train.append(1)
 else:
 y_train.append(0)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_train.append(list(w))
 if jj[i+0]-j*0.1 == jj[i-1]:
 y_train.append(1)
 else:
 y_train.append(0)

#clf = svm.SVC(kernel='linear')
clf = svm.SVC()
clf.fit(x_train, y_train)

y_test = 
x_test = 
testing = 0
for i in range(1+testing, 10+testing):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_test.append(list(w))
 if jj[i+0]+j*0.1 == jj[i-1]:
 y_test.append(1)
 else:
 y_test.append(0)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 x_test.append(list(w))
 if jj[i+0]-j*0.1 == jj[i-1]:
 y_test.append(1)
 else:
 y_test.append(0)

result = clf.predict(np.asarray(x_test))
metrics.f1_score(y_test, result, average='macro')

np.savetxt("runningresult.csv", result, delimiter=",")
np.savetxt("y_test.csv", y_test, delimiter=",")

There should be at least one one in result if it match

for i in range(1, 2):
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]+j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 if clf.predict([w]) == 1:
 print(jj[i+0]+j*0.1)
 for j in range(1, 100):
 w, v = LA.eig(np.array([[jj[i+0]-j*0.1,jj[i+1],jj[i+2]], [jj[i+3],jj[i+4],jj[i+5]],[jj[i+6],jj[i+7],jj[i+7]]]))
 if clf.predict([w]) == 1:
 print(jj[i+0]+j*0.1)

machine-learning

share|cite|improve this question

edited 5 hours ago

Khovanov

105

asked 14 hours ago

HYL

121

share|cite|improve this question

share|cite|improve this question

edited 5 hours ago

Khovanov

105

edited 5 hours ago

Khovanov

105

edited 5 hours ago

Khovanov

105

105

asked 14 hours ago

HYL

121

asked 14 hours ago

HYL

121

asked 14 hours ago

HYL

121

121

put on hold as off-topic by amWhy, Siong Thye Goh, Arnaud Mortier, max_zorn, José Carlos Santos 13 hours ago

This question appears to be off-topic. The users who voted to close gave this specific reason:

• "This question is not about mathematics, within the scope defined in the help center." – amWhy, Siong Thye Goh, Arnaud Mortier, max_zorn, José Carlos Santos

If this question can be reworded to fit the rules in the help center, please edit the question.

put on hold as off-topic by amWhy, Siong Thye Goh, Arnaud Mortier, max_zorn, José Carlos Santos 13 hours ago

This question appears to be off-topic. The users who voted to close gave this specific reason:

• "This question is not about mathematics, within the scope defined in the help center." – amWhy, Siong Thye Goh, Arnaud Mortier, max_zorn, José Carlos Santos

If this question can be reworded to fit the rules in the help center, please edit the question.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  Do you really think that anybody can understand your code, which is without any comment? Moreover, are you sure that this question is about mathematics?
  – Taroccoesbrocco
  13 hours ago
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  i add description
  – HYL
  13 hours ago
  

  
  
  
  

add a comment | 

• 
  
  
  

  
  

  
  
  
  
  
  

  
  Do you really think that anybody can understand your code, which is without any comment? Moreover, are you sure that this question is about mathematics?
  – Taroccoesbrocco
  13 hours ago
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  i add description
  – HYL
  13 hours ago
  

  
  
  
  

Do you really think that anybody can understand your code, which is without any comment? Moreover, are you sure that this question is about mathematics?
– Taroccoesbrocco
13 hours ago

Do you really think that anybody can understand your code, which is without any comment? Moreover, are you sure that this question is about mathematics?
– Taroccoesbrocco
13 hours ago

i add description
– HYL
13 hours ago

i add description
– HYL
13 hours ago

add a comment | 

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