SVM Training: prediction do not give expected result for 1 column feature

I only have observation with one feature (column) only

• The positive observation have values with 83% below 1 (so 17% above 1). Number of positive observation is 8K only
• Negative observation have values with 74% above 1 (so 16% below 1).Number of negative observation is 105KI feed those observation into matlab function fitcsvm:

svmStruct = fitcsvm(features,Y,'Standardize',true, 'Prior','uniform','KernelFunction','linear','KernelScale','auto','Verbose',1,'IterationLimit',1000000);

I expecting when I run predict to give me 1 for positives feature because it trained for most positive features are less than 1 and most negative features are greater than one. However when run predict

 [label,score,cost]= predict(svmStruct, postive_features) ;

all label are zero, which is not an expected answer. I dont know why? does SVM works when there is only 1 features?

Notice, also I got message " SVM optimization did not converge to the required tolerance." after running fitcsvm