Working with a dataset on firebase sending via python - python

I was testing a dataset I have on Firebase.
Using the this instruction
result = firebase.get('/Lot',"I") #THIS PULLS THE DATASET FROM FIREBASE
When I use the the firebase.get instruction in python I get the following.
runfile('C:/Users/Maint.Tech/parking_app/firebase_test.py',
wdir='C:/Users/Maint.Tech/parking_app')
[None, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0,
1, 0, 1, 1, 1, 0]
What is "None"? When I manipulate the numbers after "none" everything reflects in dataset correctly.
From python I am trying to take a array in python and send this via json list. How would I set up python array to reflect the correct structure to send to firebase? This is the instruction I have sent that updates the dataset correctly. Just need to figure out how to write the python right..
send_data = firebase.put('/Lot','I',[None, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0])
Thank you.


Firebase doesn't natively store arrays. When you send it an array, it stores the items from that array in number properties instead.
So if you store the follow array in JavaScript:
ref.set([ first, second, third ]);
Firebase actually stores it as:
{
"0": "first",
"1": "second",
"2": "third"
}
Now if you remove the first item from the database, and read the result back into an array in JavaScript, you get:
[ undefined, "second", "third" ]
And that last one seems very close to what you have in your Python script.
But in this case that's all just background information. It looks like you're actually sending the None yourself in the put to Firebase. If you don't want None in there, don't send it, and instead do:
send_data = firebase.put('/Lot','I',[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0])

Related

How do i replace multiple consecutive parts of an array?

So the question revolve around character segmentation. My problem is the following:
I want to segment characters, based on y-axis pixel numbers, following this ( in python) : source
What i already done to get here:
read image io.imread
swap axis np.swapaxes
sum the numbers of each column (now row) - > got y array
I got to the point where i have two arrays (both of them are exactly what I use);
x = [94, 72, 2, 2, 1, 66, 1, 13, 1, 16, 1, 8, 1, 5, 1, 47, 1, 1, 1, 3, 1, 17, 14, 87, 100]
y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
y is the thresholded binary array of the y-axis, (0 if pixel count < 1275, 1 otherwise)
x is the itertools groupby version of the y array.
I have the avarege distance of the letters too, so i know which are the wrongly segmented parts. (according to the x, the avg is 28.)
And this is the image i would like to segment, it has 4 letters, "a","l","m","a":
picture which i would like to segment
So in theory, if i could somehow merge the parts where the number of the ones are lower than the avg, and turn the "separating" zeros to ones, i should get a list which is as long as the width of the image, and has zeros only where it should have.
If i use cv.line on the y array, it indeed does segment the characters, drawing a red line where the array is 0, but it oversegments it.
oversegmented image
What i would like to do is "modify" or just re-do the y array, based on the x.
I tried a lot of methods, but i just cant find the algorithm to go over the x, find the wrong values, delete the zeros inbetween, and modify a list according to that.
My best shot is this easy, nothing-like-my-original-idea piece:
num = 0
betterarray = []
for i in range(len(y)):
if( num == 1 and y[i] == 0 and y[i+1] == 1):
betterarray.append(1)
else :
betterarray.append(y[i])
num = y[i]
It does deletes the (most of the time) one column only bad segmentations, but as I guessed, it does delete some good segmentations aswell.

You should identify the wrongly segmented letters by comparing your segments to the peak segment average and modifying the x array by combining any peak segments that are smaller than the average.
def locate_oversegmentation(array, mask, avg):
length = len(array)
for i in range(length):
// less than average peak
if (mask[i]==1 and array[i]<=avg):
if (i-2>=0):
// previous peak is less than avg
if (array[i-2]<=avg):
mask[i-1] = 1
if (i+2<=length):
// next peak is less than avg
if (array[i+2]<=avg):
mask[i+1] = 1
return mask
This function takes in array x and a compact version of array y by grouping consecutive 0's and 1's. compact_y = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0] It will return a new array changing 0's between below avg peaks to 1's. The output array is a guide to combining peaks in array x.
Example:
x = [94, 72, 2, 2, 1, 66, 1, 13, 1, 16, 1, 8, 1, 5, 1, 47, 1, 1, 1, 3, 1, 17, 14, 87, 100]
compact_y = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0]
avg = 28
guide = locate_oversegmentation(x, compact_y, avg)
>> guide = [0,1,0,1,0,1,0,1,1,1,1,1,1,1,0,1,0,1,1,1,0,1,0,1,0]
Apply the guide on array x by adding consecutive 1's together in array x.

Unusual classification results

Well I am trying to classify a data-set and i am trying to find the best way, therefore i tested multiple models to evaluate them, after double checking with accuracy and cross-validation i apply to the test data-set and unusual results coming up with the SVM model.
# Separating X and y
X = df_train.drop('*****', axis=1)
y = df_train.churn
x_test = df_test.drop('**', axis=1)
x_test.shape
# Scaling
scaler = StandardScaler()
X = pd.DataFrame(scaler.fit_transform(X), columns=list(X.columns))
#RandomOverSampler for correcting imbalanced classes
rus = RandomOverSampler(random_state=0)
X_resampled, y_resampled = rus.fit_resample(X, y)
X_resampled.shape
(7304, 14)
#Separating X and y test and train for metrics
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=20)
X_train.shape
(5843, 14)
#Classifires of Sklearn
names = ["Nearest Neighbors", "Linear SVM", "RBF SVM",
"Decision Tree", "Random Forest", "Neural Net", "AdaBoost",
"Naive Bayes", "QDA"]
gaus = 'GaussianProcessClassifier(1.0 * RBF(1.0)) , "Gaussian Process"'
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
DecisionTreeClassifier(max_depth=3),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
MLPClassifier(alpha=1, max_iter=1000),
AdaBoostClassifier(),
GaussianNB(),
QuadraticDiscriminantAnalysis()]
#Appling all models and printing results
i = 0
while i < len(classifiers):
model = classifiers[i]
model.fit(X_train, y_train)
score = model.score(X_test, y_test)
print(names[i], ", Accuraccy: ", accuracy_score(y_test, model.predict(X_test)))
i += 1
​
Nearest Neighbors , Accuraccy: 0.9240246406570842
Linear SVM , Accuraccy: 0.7809719370294319
RBF SVM , Accuraccy: 0.997946611909651
Decision Tree , Accuraccy: 0.8446269678302533
Random Forest , Accuraccy: 0.8042436687200547
Neural Net , Accuraccy: 0.891170431211499
AdaBoost , Accuraccy: 0.8473648186173853
Naive Bayes , Accuraccy: 0.8220396988364134
QDA , Accuraccy: 0.8576317590691307
#Best model from the comparison
best = SVC(gamma=2, C=1)
best.fit(X_resampled, y_resampled)
print(cross_val_score(best, X_resampled, y_resampled, cv=5))
​
best.predict(x_test)
[0.99657769 1. 1. 1. 1. ]
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0], dtype=int64)
First unusual results all zeros even tho that the acc annd cross-val seems ok
#Best model from the comparison
best = SVC(kernel="linear", C=0.021)
best.fit(X_resampled, y_resampled)
print(cross_val_score(best, X_resampled, y_resampled, cv=5))
​
best.predict(x_test)
[0.77412731 0.77275838 0.77549624 0.77275838 0.76986301]
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1], dtype=int64)
Second unusual , even tho i have around 77% acc, even if the first model was the "perfect" one, we had to expect zeros, similar results (all 1) produces the KNN, but other examples produce more "logical" results like:
#Best model from the comparison
best = DecisionTreeClassifier(max_depth=3)
best.fit(X_resampled, y_resampled)
print(cross_val_score(best, X_resampled, y_resampled, cv=5))
​
best.predict(x_test)
[0.8275154 0.83093771 0.82683094 0.8275154 0.83493151]
array([0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0,
0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1,
0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0,
0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,
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0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1,
1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,
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1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,
0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0,
1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,
0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0,
0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,
0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,
1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,
0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1,
1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0,
0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1,
1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1], dtype=int64)
I really don't know what i am doing wrong , i will appreciate the help!
Thank you!

How to find the difference of elements within an array of 0 and 1

I currently have this array.
idx_binary = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
I'm trying to find all the instances where the difference between two elements in this array is 1 (so when it goes from 0 to 1).
This is what I currently have for that:
threshCross_idx = np.where(np.diff(idx_binary) == 1)
However, this is giving me all the instances of 1 ((array([48], dtype=int64))). Can anyone shed light on what I'm doing incorrectly? I'd expect the output to be 1, since there's only one instance where the difference between two elements is 1.

Period of a sequence with python pycogent

While working with a problem, I faced with determining the period of a sequence in Python. I faced with the cogent library
[0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0]
How can use the cogent library to determine the period of my sequence?
What are exactly the pwr and period values returned from these DFT or auto_corr.

Poor Skill of Support Vector Machine in Python compared to R

I am trying to roughly reproduce C-classification SVM cross-validation skill in R (e1071 package) using Python (scikit-learn), but am getting nowhere near R's prediction skill. Given the below training and test data (which have been smoothed from datasets much larger in length), R prediction skill is 0.87 (where 1 is perfect), and Python skill is 0.55 which is not much better than guessing. Note that I am not at all trying to get identical results, I am just hopeful that if R can do reasonably well then so could Python on the same dataset. I have split my data 50-50 (training and test), and am trying to predict binomial results from floats. R and Python code are given below. All the default SVM arguments that I checked were the same between both R and Python (gamma, C(cost), shrinking, tol, etc).
R code:
library("e1071")
data <- c(-108.604150711185, -131.880188127745, -18.3017441809734, 32.011639982337, -71.6651360870381, -107.587087751331, 21.316311739316, -36.015324564807, 138.22302265079, 47.9322592065447, -129.007749732555, -150.41808326425, -141.00589707504, -105.912063885407, 76.2956568174239, 141.457541434218, -20.6676395937811, -226.505644333494, -151.229861588686, -160.18717733968, -107.01667849677, -7.52794131287047, -93.1147621027003, 5.59630172385392, 38.741091785708, -32.9061390503546, -78.5031246062325, -9.64080356337477, -54.1430873201472, -108.127067430103, -12.2589074567133, 129.212940940854, 132.670728015743, 107.075153550768, 167.176831103164, -20.6839530330714, 102.677911281291, -109.423698849103, -154.454318421757, 140.52342226202, 110.184351332211, -16.6842057565239, -11.1688984829787, 178.441845032635, 37.0689292040101, 166.610506783818, -79.2764182099804, 99.1136693164655, 82.0929274697289, 15.1752041486536, 178.489001782771, 145.332200036106, -185.977800430997, -90.5440753976243, 78.0459300120412, 144.297553387967, 99.5945824957091, 110.803195137024, 81.3094331750562, -396.825240330405, -166.038928089807, -78.863983688682, 138.309908804212, -148.647304302406, -2.23135233624276, 129.411511929621, -111.664324254549, -96.4151180340831, 129.219227225386, 90.7050615157428, 141.986869866474, 93.0147970463941, 142.807435791073, -75.8426755946232, 122.537973092667, 117.078515092191, 134.166968023265, 90.8512172789568, 146.367129646428, 125.539182526718, -70.485058023267, -46.967575223949, 116.210349687502, -91.2992704167832, 104.052231138142, -114.580693287221, -82.9991067628608, -111.649187979413)
class <- as.factor(c(0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0))
df_training <- data.frame(data, class)
data <- c(133.75999742845, 22.9386702890105, -126.959902277009, -116.317935595297, -33.9418594804197, -49.0102540773413, -159.266630498512, -8.92296705690401, 114.328300224712, 66.0706175847251, -154.385344188283, 70.7868284982941, -28.334490887314, 118.755307949047, 154.362286178401, 101.331675190569, 96.2196681290104, 99.5694296232446, 210.160787371823, 65.8474210711036, -125.475676456606, 66.7541385125748, -161.001356357477, -40.1416817172267, 38.6877489907967, -7.12706914419719, -10.3967176519225, -80.6831091111636, 128.604227270616, 75.4219966516171, 184.951786958864, 90.9170782990185, 66.7190886024699, 81.377280661573, -82.4053965286415, -65.6718687269108, 61.1679518726262, 190.532649096311, 199.917670153196, 104.558442558929, 113.747065157369, 106.640501329133, 80.593201532054, 75.0176280888154, 155.538654396817, 30.0548798029353, 116.900219512636, 131.431417509576, 33.3308447581156, -121.191534016935, -80.4203785670198, 157.737407847885, 66.5956228628815, 50.8340706561446, -113.713450848071, -18.7787225270887, 113.832326071127, -45.5884280143408, 221.782395098832, 70.1660982367319, 235.005982636939, 80.8180320055801, -74.7107276814795, 133.925782624001, 97.9261686360971, -127.954532027281, 58.9295075974962, 96.1702797891484, -49.6048543914143, -42.1842037639683, -235.694708213157, 13.4862841916787, 126.396462591781, 214.297316240176, 125.148658464391, 84.8887673204376, 78.2717096234718, 139.677936314095, -168.649300541479, 103.40253638232, 69.2727189156141, 153.017155534869, -238.07168745534, -166.929968475244, 113.414489211719, 85.5520123243496, 120.582346886614, -214.850084749638, 96.8090523924549)
class <- as.factor(c(1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1))
df_test <- data.frame(data, class)
#train model
best.svm <- best.tune(svm,
class~data,
data=df_training,kernel = 'radial',cost = 1, gamma = 0.01,
type = "C-classification")
#make predictions
TrainingPredictions<-predict(best.svm,df_training,type="class")
TestPredictions <- predict(best.svm,df_test,type="class")
Skill = sum(TestPredictions==df_test[[c('class')]])/length(TestPredictions)
print(Skill) #value is 0.87
Python code:
import numpy as np
from sklearn.svm import SVC
Data = np.array([-108.604150711185, -131.880188127745,-18.3017441809734, 32.011639982337, -71.6651360870381, -107.587087751331, 21.316311739316, -36.015324564807, 138.22302265079, 47.9322592065447, -129.007749732555, -150.41808326425, -141.00589707504, -105.912063885407, 76.2956568174239, 141.457541434218, -20.6676395937811, -226.505644333494, -151.229861588686, -160.18717733968, -107.01667849677, -7.52794131287047, -93.1147621027003, 5.59630172385392, 38.741091785708, -32.9061390503546, -78.5031246062325, -9.64080356337477, -54.1430873201472, -108.127067430103, -12.2589074567133, 129.212940940854, 132.670728015743, 107.075153550768, 167.176831103164, -20.6839530330714, 102.677911281291, -109.423698849103, -154.454318421757, 140.52342226202, 110.184351332211, -16.6842057565239, -11.1688984829787, 178.441845032635, 37.0689292040101, 166.610506783818, -79.2764182099804, 99.1136693164655, 82.0929274697289, 15.1752041486536, 178.489001782771, 145.332200036106, -185.977800430997, -90.5440753976243, 78.0459300120412, 144.297553387967, 99.5945824957091, 110.803195137024, 81.3094331750562,-396.825240330405, -166.038928089807, -78.863983688682, 138.309908804212, -148.647304302406, -2.23135233624276, 129.411511929621, -111.664324254549, -96.4151180340831, 129.219227225386, 90.7050615157428, 141.986869866474, 93.0147970463941, 142.807435791073, -75.8426755946232, 122.537973092667, 117.078515092191, 134.166968023265, 90.8512172789568, 146.367129646428, 125.539182526718, -70.485058023267, -46.967575223949, 116.210349687502, -91.2992704167832, 104.052231138142, -114.580693287221, -82.9991067628608, -111.649187979413])
Class = np.array([0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0])
df_training = np.array([Data, Class])
Data = np.array([133.75999742845, 22.9386702890105, -126.959902277009, -116.317935595297, -33.9418594804197, -49.0102540773413, -159.266630498512, -8.92296705690401, 114.328300224712, 66.0706175847251, -154.385344188283, 70.7868284982941, -28.334490887314, 118.755307949047, 154.362286178401, 101.331675190569, 96.2196681290104, 99.5694296232446, 210.160787371823, 65.8474210711036, -125.475676456606, 66.7541385125748, -161.001356357477, -40.1416817172267, 38.6877489907967, -7.12706914419719, -10.3967176519225, -80.6831091111636, 128.604227270616, 75.4219966516171, 184.951786958864, 90.9170782990185, 66.7190886024699, 81.377280661573, -82.4053965286415, -65.6718687269108, 61.1679518726262, 190.532649096311, 199.917670153196, 104.558442558929, 113.747065157369, 106.640501329133,80.593201532054, 75.0176280888154, 155.538654396817, 30.0548798029353, 116.900219512636, 131.431417509576, 33.3308447581156, -121.191534016935, -80.4203785670198, 157.737407847885, 66.5956228628815, 50.8340706561446, -113.713450848071, -18.7787225270887, 113.832326071127, -45.5884280143408, 221.782395098832, 70.1660982367319, 235.005982636939, 80.8180320055801, -74.7107276814795, 133.925782624001, 97.9261686360971, -127.954532027281, 58.9295075974962, 96.1702797891484, -49.6048543914143, -42.1842037639683, -235.694708213157, 13.4862841916787, 126.396462591781, 214.297316240176, 125.148658464391, 84.8887673204376, 78.2717096234718, 139.677936314095, -168.649300541479, 103.40253638232, 69.2727189156141, 153.017155534869, -238.07168745534, -166.929968475244, 113.414489211719,85.5520123243496, 120.582346886614, -214.850084749638, 96.8090523924549])
Class = np.array([1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1])
df_test = np.array([Data, Class])
# train model
clf = SVC(verbose=True, gamma=0.01, kernel='rbf', C=1)
# make predictions
clf.fit(df_training[0].reshape(88,1), df_training[1].reshape(88,1))
TrainingPredictions = clf.predict(df_training[0].reshape(88,1))
TestPredictions = clf.predict(df_test[0].reshape(89,1))
Skill = np.sum(TestPredictions==df_test[1])/float(len(TestPredictions))
print Skill #value is 0.55

This observed difference might come from the fact that in R, svm() scales the data by default (see documentation, page 6).
If you use scikit-learn's StandardScaler, you end up with a result pretty close from the one you obtained with R :
import numpy as np
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
Data = np.array([-108.604150711185, -131.880188127745,-18.3017441809734, 32.011639982337, -71.6651360870381, -107.587087751331, 21.316311739316, -36.015324564807, 138.22302265079, 47.9322592065447, -129.007749732555, -150.41808326425, -141.00589707504, -105.912063885407, 76.2956568174239, 141.457541434218, -20.6676395937811, -226.505644333494, -151.229861588686, -160.18717733968, -107.01667849677, -7.52794131287047, -93.1147621027003, 5.59630172385392, 38.741091785708, -32.9061390503546, -78.5031246062325, -9.64080356337477, -54.1430873201472, -108.127067430103, -12.2589074567133, 129.212940940854, 132.670728015743, 107.075153550768, 167.176831103164, -20.6839530330714, 102.677911281291, -109.423698849103, -154.454318421757, 140.52342226202, 110.184351332211, -16.6842057565239, -11.1688984829787, 178.441845032635, 37.0689292040101, 166.610506783818, -79.2764182099804, 99.1136693164655, 82.0929274697289, 15.1752041486536, 178.489001782771, 145.332200036106, -185.977800430997, -90.5440753976243, 78.0459300120412, 144.297553387967, 99.5945824957091, 110.803195137024, 81.3094331750562,-396.825240330405, -166.038928089807, -78.863983688682, 138.309908804212, -148.647304302406, -2.23135233624276, 129.411511929621, -111.664324254549, -96.4151180340831, 129.219227225386, 90.7050615157428, 141.986869866474, 93.0147970463941, 142.807435791073, -75.8426755946232, 122.537973092667, 117.078515092191, 134.166968023265, 90.8512172789568, 146.367129646428, 125.539182526718, -70.485058023267, -46.967575223949, 116.210349687502, -91.2992704167832, 104.052231138142, -114.580693287221, -82.9991067628608, -111.649187979413])
Data = scaler.fit_transform(Data)
Class = np.array([0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0])
df_training = np.array([Data, Class])
Data = np.array([133.75999742845, 22.9386702890105, -126.959902277009, -116.317935595297, -33.9418594804197, -49.0102540773413, -159.266630498512, -8.92296705690401, 114.328300224712, 66.0706175847251, -154.385344188283, 70.7868284982941, -28.334490887314, 118.755307949047, 154.362286178401, 101.331675190569, 96.2196681290104, 99.5694296232446, 210.160787371823, 65.8474210711036, -125.475676456606, 66.7541385125748, -161.001356357477, -40.1416817172267, 38.6877489907967, -7.12706914419719, -10.3967176519225, -80.6831091111636, 128.604227270616, 75.4219966516171, 184.951786958864, 90.9170782990185, 66.7190886024699, 81.377280661573, -82.4053965286415, -65.6718687269108, 61.1679518726262, 190.532649096311, 199.917670153196, 104.558442558929, 113.747065157369, 106.640501329133,80.593201532054, 75.0176280888154, 155.538654396817, 30.0548798029353, 116.900219512636, 131.431417509576, 33.3308447581156, -121.191534016935, -80.4203785670198, 157.737407847885, 66.5956228628815, 50.8340706561446, -113.713450848071, -18.7787225270887, 113.832326071127, -45.5884280143408, 221.782395098832, 70.1660982367319, 235.005982636939, 80.8180320055801, -74.7107276814795, 133.925782624001, 97.9261686360971, -127.954532027281, 58.9295075974962, 96.1702797891484, -49.6048543914143, -42.1842037639683, -235.694708213157, 13.4862841916787, 126.396462591781, 214.297316240176, 125.148658464391, 84.8887673204376, 78.2717096234718, 139.677936314095, -168.649300541479, 103.40253638232, 69.2727189156141, 153.017155534869, -238.07168745534, -166.929968475244, 113.414489211719,85.5520123243496, 120.582346886614, -214.850084749638, 96.8090523924549])
Data = scaler.fit_transform(Data)
Class = np.array([1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1])
df_test = np.array([Data, Class])
# train model
clf = SVC(verbose=True, gamma=0.01, kernel='rbf', C=1)
# make predictions
clf.fit(df_training[0].reshape(88,1), df_training[1].reshape(88,1))
TrainingPredictions = clf.predict(df_training[0].reshape(88,1))
TestPredictions = clf.predict(df_test[0].reshape(89,1))
Skill = np.sum(TestPredictions==df_test[1])/float(len(TestPredictions))
print("Skill: "+str(Skill)) #value is 0.84

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