title: float() argument must be a string or a number in Stochastic Gradient Descent of scikit learn
body: <p>I am trying to use svm classifier for text classification, </p>
  
  <pre><code>self.clf = linear_model.SGDClassifier(alpha=1e-3,loss="hinge", penalty="l2")
  data_folder = self.root_dir + "/trec_data"
  train_dataset = load_files(data_folder)
  for text in self.data_train.data:
      text = self.modifyQuery(text.decode('utf-8','ignore'))
      training_data.append(text)
  raw_X = (self.token_ques(text) for text in training_data)
  rbf_feature = RBFSampler(gamma=1, random_state=1)
  X_train = rbf_feature.fit_transform(raw_X)
  y_train = self.data_train.target
  </code></pre>
  
  <p>which gives me error at <code>X_train = rbf_feature.fit_transform(raw_X)</code>. </p>
  
  <pre><code>Traceback (most recent call last):
    File "classifier_lessClass.py", line 167, in trainClassifier
      X_train = rbf_feature.fit_transform(np.array(raw_X))
    File "/usr/local/lib/python2.7/dist-packages/scikit_learn-0.16b1-py2.7-linux-i686.egg/sklearn/base.py", line 433, in fit_transform
      return self.fit(X, **fit_params).transform(X)
    File "/usr/local/lib/python2.7/dist-packages/scikit_learn-0.16b1-py2.7-linux-i686.egg/sklearn/kernel_approximation.py", line 76, in fit
      X = check_array(X, accept_sparse='csr')
    File "/usr/local/lib/python2.7/dist-packages/scikit_learn-0.16b1-py2.7-linux-i686.egg/sklearn/utils/validation.py", line 341, in check_array
      array = np.array(array, dtype=dtype, order=order, copy=copy)
  TypeError: float() argument must be a string or a number
  </code></pre>
  
  <p>I tried with converting <code>raw_X</code> to float() but still same error. Also tried with <code>np.array(raw_X)</code> which gives same error.</p>