You are just supposed to provide the predict method with the same 2D array, but with one value that you want to process (or more). In short, you can just replace
[0.58,0.76]
With
[[0.58,0.76]]
And it should work.
EDIT: This answer became popular so I thought I’d add a little more explanation about ML. The short version: we can only use predict on data that is of the same dimensionality as the training data (X) was.
In the example in question, we give the computer a bunch of rows in X (with 2 values each) and we show it the correct responses in y. When we want to predict using new values, our program expects the same – a bunch of rows. Even if we want to do it to just one row (with two values), that row has to be part of another array.
The problem is occurring when you run prediction on the array [0.58,0.76]. Fix the problem by reshaping it before you call predict():
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
style.use(“ggplot”)
from sklearn import svm
x = [1, 5, 1.5, 8, 1, 9]
y = [2, 8, 1.8, 8, 0.6, 11]
plt.scatter(x,y)
plt.show()
X = np.array([[1,2],
[5,8],
[1.5,1.8],
[8,8],
[1,0.6],
[9,11]])
y = [0,1,0,1,0,1]
clf = svm.SVC(kernel=’linear’, C = 1.0)
clf.fit(X,y)
test = np.array([0.58, 0.76])
print test # Produces: [ 0.58 0.76]
print test.shape # Produces: (2,) meaning 2 rows, 1 col
test = test.reshape(1, -1)
print test # Produces: [[ 0.58 0.76]]
print test.shape # Produces (1, 2) meaning 1 row, 2 cols
print(clf.predict(test)) # Produces [0], as expected