[[0.04954833 0.09270993 0.08942692 ... 0.48863458 0.73213733 0.06461511]
 [0.67277258 0.73228559 0.38106843 ... 0.19733594 0.46565561 0.0633227 ]
 [0.76832175 0.72083736 0.57405175 ... 0.20544414 0.48300792 0.04166638]
 ...
 [0.10236642 0.16945559 0.11649592 ... 0.45456484 0.6571285  0.06879472]
 [0.23305677 0.30059045 0.13161543 ... 0.49642585 0.70336119 0.08459844]
 [0.30021881 0.43048649 0.13444188 ... 0.35319312 0.58004879 0.07861176]]

svm.fit(x_train, y_train)

ValueError: Expected 2D array, got 1D array instead:
array=[138.45058667 103.40711982  31.4742806   41.2995549   26.70719845
  15.62063607  -2.32301952  -2.42220695   1.73629153].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

meann = self.getMean(r_filtered, g_filtered, b_filtered)
std = self.getStd(r_filtered, g_filtered, b_filtered)
skewness = self.getSkewness(r_filtered, g_filtered, b_filtered)

#return 9 features
return list(np.array(np.concatenate([meann, std, skewness])))

scaler = MinMaxScaler(feature_range=(0, 1))
rescaled_features = scaler.fit_transform(features_array)

labels.append(label)
caracteristicas.append(moments)