--- a
+++ b/eeg_learn_functions.py
@@ -0,0 +1,496 @@
+from __future__ import print_function
+import time
+
+import numpy as np
+np.random.seed(1234)
+from functools import reduce
+import math as m
+
+import scipy.io
+#import theano
+#import theano.tensor as T
+
+from scipy.interpolate import griddata
+from sklearn.preprocessing import scale
+#from utils import augment_EEG, cart2sph, pol2cart
+
+#import lasagne
+# from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
+#from lasagne.layers import Conv2DLayer, MaxPool2DLayer, InputLayer
+#from lasagne.layers import DenseLayer, ElemwiseMergeLayer, FlattenLayer
+#from lasagne.layers import ConcatLayer, ReshapeLayer, get_output_shape
+#from lasagne.layers import Conv1DLayer, DimshuffleLayer, LSTMLayer, SliceLayer
+
+
+def azim_proj(pos):
+    """
+    Computes the Azimuthal Equidistant Projection of input point in 3D Cartesian Coordinates.
+    Imagine a plane being placed against (tangent to) a globe. If
+    a light source inside the globe projects the graticule onto
+    the plane the result would be a planar, or azimuthal, map
+    projection.
+
+    :param pos: position in 3D Cartesian coordinates
+    :return: projected coordinates using Azimuthal Equidistant Projection
+    """
+    [r, elev, az] = cart2sph(pos[0], pos[1], pos[2])
+    return pol2cart(az, m.pi / 2 - elev)
+
+
+def gen_images(locs, features, n_gridpoints, normalize=True,
+               augment=False, pca=False, std_mult=0.1, n_components=2, edgeless=False):
+    """
+    Generates EEG images given electrode locations in 2D space and multiple feature values for each electrode
+
+    :param locs: An array with shape [n_electrodes, 2] containing X, Y
+                        coordinates for each electrode.
+    :param features: Feature matrix as [n_samples, n_features]
+                                Features are as columns.
+                                Features corresponding to each frequency band are concatenated.
+                                (alpha1, alpha2, ..., beta1, beta2,...)
+    :param n_gridpoints: Number of pixels in the output images
+    :param normalize:   Flag for whether to normalize each band over all samples
+    :param augment:     Flag for generating augmented images
+    :param pca:         Flag for PCA based data augmentation
+    :param std_mult     Multiplier for std of added noise
+    :param n_components: Number of components in PCA to retain for augmentation
+    :param edgeless:    If True generates edgeless images by adding artificial channels
+                        at four corners of the image with value = 0 (default=False).
+    :return:            Tensor of size [samples, colors, W, H] containing generated
+                        images.
+    """
+    feat_array_temp = []
+    nElectrodes = locs.shape[0]     # Number of electrodes
+    # Test whether the feature vector length is divisible by number of electrodes
+    assert features.shape[1] % nElectrodes == 0
+    n_colors = features.shape[1] // nElectrodes
+    for c in range(int(n_colors)):
+        feat_array_temp.append(features[:, c * nElectrodes : nElectrodes * (c+1)])
+    if augment:
+        if pca:
+            for c in range(n_colors):
+                feat_array_temp[c] = augment_EEG(feat_array_temp[c], std_mult, pca=True, n_components=n_components)
+        else:
+            for c in range(n_colors):
+                feat_array_temp[c] = augment_EEG(feat_array_temp[c], std_mult, pca=False, n_components=n_components)
+    nSamples = features.shape[0]
+    # Interpolate the values
+    grid_x, grid_y = np.mgrid[
+                     min(locs[:, 0]):max(locs[:, 0]):n_gridpoints*1j,
+                     min(locs[:, 1]):max(locs[:, 1]):n_gridpoints*1j
+                     ]
+    temp_interp = []
+    for c in range(n_colors):
+        temp_interp.append(np.zeros([nSamples, n_gridpoints, n_gridpoints]))
+    # Generate edgeless images
+    if edgeless:
+        min_x, min_y = np.min(locs, axis=0)
+        max_x, max_y = np.max(locs, axis=0)
+        locs = np.append(locs, np.array([[min_x, min_y], [min_x, max_y],[max_x, min_y],[max_x, max_y]]),axis=0)
+        for c in range(n_colors):
+            feat_array_temp[c] = np.append(feat_array_temp[c], np.zeros((nSamples, 4)), axis=1)
+    # Interpolating
+    for i in range(nSamples):
+        for c in range(n_colors):
+            temp_interp[c][i, :, :] = griddata(locs, feat_array_temp[c][i, :], (grid_x, grid_y),
+                                    method='cubic', fill_value=np.nan)
+        print('Interpolating {0}/{1}\r'.format(i+1, nSamples), end='\r')
+    # Normalizing
+    for c in range(n_colors):
+        if normalize:
+            temp_interp[c][~np.isnan(temp_interp[c])] = \
+                scale(temp_interp[c][~np.isnan(temp_interp[c])])
+        temp_interp[c] = np.nan_to_num(temp_interp[c])
+    return np.swapaxes(np.asarray(temp_interp), 0, 1)     # swap axes to have [samples, colors, W, H]
+
+
+def build_cnn(input_var=None, w_init=None, n_layers=(4, 2, 1), n_filters_first=32, imsize=32, n_colors=3):
+    """
+    Builds a VGG style CNN network followed by a fully-connected layer and a softmax layer.
+    Stacks are separated by a maxpool layer. Number of kernels in each layer is twice
+    the number in previous stack.
+    input_var: Theano variable for input to the network
+    outputs: pointer to the output of the last layer of network (softmax)
+
+    :param input_var: theano variable as input to the network
+    :param w_init: Initial weight values
+    :param n_layers: number of layers in each stack. An array of integers with each
+                    value corresponding to the number of layers in each stack.
+                    (e.g. [4, 2, 1] == 3 stacks with 4, 2, and 1 layers in each.
+    :param n_filters_first: number of filters in the first layer
+    :param imSize: Size of the image
+    :param n_colors: Number of color channels (depth)
+    :return: a pointer to the output of last layer
+    """
+    weights = []        # Keeps the weights for all layers
+    count = 0
+    # If no initial weight is given, initialize with GlorotUniform
+    if w_init is None:
+        w_init = [lasagne.init.GlorotUniform()] * sum(n_layers)
+    # Input layer
+    network = InputLayer(shape=(None, n_colors, imsize, imsize),
+                                        input_var=input_var)
+    for i, s in enumerate(n_layers):
+        for l in range(s):
+            network = Conv2DLayer(network, num_filters=n_filters_first * (2 ** i), filter_size=(3, 3),
+                          W=w_init[count], pad='same')
+            count += 1
+            weights.append(network.W)
+        network = MaxPool2DLayer(network, pool_size=(2, 2))
+    return network, weights
+
+
+def build_convpool_max(input_vars, nb_classes, imsize=32, n_colors=3, n_timewin=3):
+    """
+    Builds the complete network with maxpooling layer in time.
+
+    :param input_vars: list of EEG images (one image per time window)
+    :param nb_classes: number of classes
+    :param imsize: size of the input image (assumes a square input)
+    :param n_colors: number of color channels in the image
+    :param n_timewin: number of time windows in the snippet
+    :return: a pointer to the output of last layer
+    """
+    convnets = []
+    w_init = None
+    # Build 7 parallel CNNs with shared weights
+    for i in range(n_timewin):
+        if i == 0:
+            convnet, w_init = build_cnn(input_vars[i], imsize=imsize, n_colors=n_colors)
+        else:
+            convnet, _ = build_cnn(input_vars[i], w_init=w_init, imsize=imsize, n_colors=n_colors)
+        convnets.append(convnet)
+    # convpooling using Max pooling over frames
+    convpool = ElemwiseMergeLayer(convnets, theano.tensor.maximum)
+    # A fully-connected layer of 512 units with 50% dropout on its inputs:
+    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),
+            num_units=512, nonlinearity=lasagne.nonlinearities.rectify)
+    # And, finally, the output layer with 50% dropout on its inputs:
+    convpool = lasagne.layers.DenseLayer(lasagne.layers.dropout(convpool, p=.5),
+            num_units=nb_classes, nonlinearity=lasagne.nonlinearities.softmax)
+    return convpool
+
+
+def build_convpool_conv1d(input_vars, nb_classes, imsize=32, n_colors=3, n_timewin=3):
+    """
+    Builds the complete network with 1D-conv layer to integrate time from sequences of EEG images.
+
+    :param input_vars: list of EEG images (one image per time window)
+    :param nb_classes: number of classes
+    :param imsize: size of the input image (assumes a square input)
+    :param n_colors: number of color channels in the image
+    :param n_timewin: number of time windows in the snippet
+    :return: a pointer to the output of last layer
+    """
+    convnets = []
+    w_init = None
+    # Build 7 parallel CNNs with shared weights
+    for i in range(n_timewin):
+        if i == 0:
+            convnet, w_init = build_cnn(input_vars[i], imsize=imsize, n_colors=n_colors)
+        else:
+            convnet, _ = build_cnn(input_vars[i], w_init=w_init, imsize=imsize, n_colors=n_colors)
+        convnets.append(FlattenLayer(convnet))
+    # at this point convnets shape is [numTimeWin][n_samples, features]
+    # we want the shape to be [n_samples, features, numTimeWin]
+    convpool = ConcatLayer(convnets)
+    convpool = ReshapeLayer(convpool, ([0], n_timewin, get_output_shape(convnets[0])[1]))
+    convpool = DimshuffleLayer(convpool, (0, 2, 1))
+    # input to 1D convlayer should be in (batch_size, num_input_channels, input_length)
+    convpool = Conv1DLayer(convpool, 64, 3)
+    # A fully-connected layer of 512 units with 50% dropout on its inputs:
+    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),
+            num_units=512, nonlinearity=lasagne.nonlinearities.rectify)
+    # And, finally, the output layer with 50% dropout on its inputs:
+    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),
+            num_units=nb_classes, nonlinearity=lasagne.nonlinearities.softmax)
+    return convpool
+
+
+def build_convpool_lstm(input_vars, nb_classes, grad_clip=110, imsize=32, n_colors=3, n_timewin=3):
+    """
+    Builds the complete network with LSTM layer to integrate time from sequences of EEG images.
+
+    :param input_vars: list of EEG images (one image per time window)
+    :param nb_classes: number of classes
+    :param grad_clip:  the gradient messages are clipped to the given value during
+                        the backward pass.
+    :param imsize: size of the input image (assumes a square input)
+    :param n_colors: number of color channels in the image
+    :param n_timewin: number of time windows in the snippet
+    :return: a pointer to the output of last layer
+    """
+    convnets = []
+    w_init = None
+    # Build 7 parallel CNNs with shared weights
+    for i in range(n_timewin):
+        if i == 0:
+            convnet, w_init = build_cnn(input_vars[i], imsize=imsize, n_colors=n_colors)
+        else:
+            convnet, _ = build_cnn(input_vars[i], w_init=w_init, imsize=imsize, n_colors=n_colors)
+        convnets.append(FlattenLayer(convnet))
+    # at this point convnets shape is [numTimeWin][n_samples, features]
+    # we want the shape to be [n_samples, features, numTimeWin]
+    convpool = ConcatLayer(convnets)
+    convpool = ReshapeLayer(convpool, ([0], n_timewin, get_output_shape(convnets[0])[1]))
+    # Input to LSTM should have the shape as (batch size, SEQ_LENGTH, num_features)
+    convpool = LSTMLayer(convpool, num_units=128, grad_clipping=grad_clip,
+        nonlinearity=lasagne.nonlinearities.tanh)
+    # We only need the final prediction, we isolate that quantity and feed it
+    # to the next layer.
+    convpool = SliceLayer(convpool, -1, 1)      # Selecting the last prediction
+    # A fully-connected layer of 256 units with 50% dropout on its inputs:
+    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),
+            num_units=256, nonlinearity=lasagne.nonlinearities.rectify)
+    # And, finally, the output layer with 50% dropout on its inputs:
+    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),
+            num_units=nb_classes, nonlinearity=lasagne.nonlinearities.softmax)
+    return convpool
+
+
+def build_convpool_mix(input_vars, nb_classes, grad_clip=110, imsize=32, n_colors=3, n_timewin=3):
+    """
+    Builds the complete network with LSTM and 1D-conv layers combined
+
+    :param input_vars: list of EEG images (one image per time window)
+    :param nb_classes: number of classes
+    :param grad_clip:  the gradient messages are clipped to the given value during
+                        the backward pass.
+    :param imsize: size of the input image (assumes a square input)
+    :param n_colors: number of color channels in the image
+    :param n_timewin: number of time windows in the snippet
+    :return: a pointer to the output of last layer
+    """
+    convnets = []
+    w_init = None
+    # Build 7 parallel CNNs with shared weights
+    for i in range(n_timewin):
+        if i == 0:
+            convnet, w_init = build_cnn(input_vars[i], imsize=imsize, n_colors=n_colors)
+        else:
+            convnet, _ = build_cnn(input_vars[i], w_init=w_init, imsize=imsize, n_colors=n_colors)
+        convnets.append(FlattenLayer(convnet))
+    # at this point convnets shape is [numTimeWin][n_samples, features]
+    # we want the shape to be [n_samples, features, numTimeWin]
+    convpool = ConcatLayer(convnets)
+    convpool = ReshapeLayer(convpool, ([0], n_timewin, get_output_shape(convnets[0])[1]))
+    reformConvpool = DimshuffleLayer(convpool, (0, 2, 1))
+    # input to 1D convlayer should be in (batch_size, num_input_channels, input_length)
+    conv_out = Conv1DLayer(reformConvpool, 64, 3)
+    conv_out = FlattenLayer(conv_out)
+    # Input to LSTM should have the shape as (batch size, SEQ_LENGTH, num_features)
+    lstm = LSTMLayer(convpool, num_units=128, grad_clipping=grad_clip,
+        nonlinearity=lasagne.nonlinearities.tanh)
+    lstm_out = SliceLayer(lstm, -1, 1)
+    # Merge 1D-Conv and LSTM outputs
+    dense_input = ConcatLayer([conv_out, lstm_out])
+    # A fully-connected layer of 256 units with 50% dropout on its inputs:
+    convpool = DenseLayer(lasagne.layers.dropout(dense_input, p=.5),
+            num_units=512, nonlinearity=lasagne.nonlinearities.rectify)
+    # And, finally, the 10-unit output layer with 50% dropout on its inputs:
+    convpool = DenseLayer(convpool,
+            num_units=nb_classes, nonlinearity=lasagne.nonlinearities.softmax)
+    return convpool
+
+
+def iterate_minibatches(inputs, targets, batchsize, shuffle=False):
+    """
+    Iterates over the samples returing batches of size batchsize.
+    :param inputs: input data array. It should be a 4D numpy array for images [n_samples, n_colors, W, H] and 5D numpy
+                    array if working with sequence of images [n_timewindows, n_samples, n_colors, W, H].
+    :param targets: vector of target labels.
+    :param batchsize: Batch size
+    :param shuffle: Flag whether to shuffle the samples before iterating or not.
+    :return: images and labels for a batch
+    """
+    if inputs.ndim == 4:
+        input_len = inputs.shape[0]
+    elif inputs.ndim == 5:
+        input_len = inputs.shape[1]
+    assert input_len == len(targets)
+    if shuffle:
+        indices = np.arange(input_len)
+        np.random.shuffle(indices)
+    for start_idx in range(0, input_len, batchsize):
+        if shuffle:
+            excerpt = indices[start_idx:start_idx + batchsize]
+        else:
+            excerpt = slice(start_idx, start_idx + batchsize)
+        if inputs.ndim == 4:
+            yield inputs[excerpt], targets[excerpt]
+        elif inputs.ndim == 5:
+            yield inputs[:, excerpt], targets[excerpt]
+
+
+def train(images, labels, fold, model_type, batch_size=32, num_epochs=5):
+    """
+    A sample training function which loops over the training set and evaluates the network
+    on the validation set after each epoch. Evaluates the network on the training set
+    whenever the
+    :param images: input images
+    :param labels: target labels
+    :param fold: tuple of (train, test) index numbers
+    :param model_type: model type ('cnn', '1dconv', 'maxpool', 'lstm', 'mix')
+    :param batch_size: batch size for training
+    :param num_epochs: number of epochs of dataset to go over for training
+    :return: none
+    """
+    num_classes = len(np.unique(labels))
+    (X_train, y_train), (X_val, y_val), (X_test, y_test) = reformatInput(images, labels, fold)
+    X_train = X_train.astype("float32", casting='unsafe')
+    X_val = X_val.astype("float32", casting='unsafe')
+    X_test = X_test.astype("float32", casting='unsafe')
+    # Prepare Theano variables for inputs and targets
+    input_var = T.TensorType('floatX', ((False,) * 5))()
+    target_var = T.ivector('targets')
+    # Create neural network model (depending on first command line parameter)
+    print("Building model and compiling functions...")
+    # Building the appropriate model
+    if model_type == '1dconv':
+        network = build_convpool_conv1d(input_var, num_classes)
+    elif model_type == 'maxpool':
+        network = build_convpool_max(input_var, num_classes)
+    elif model_type == 'lstm':
+        network = build_convpool_lstm(input_var, num_classes, 100)
+    elif model_type == 'mix':
+        network = build_convpool_mix(input_var, num_classes, 100)
+    elif model_type == 'cnn':
+        input_var = T.tensor4('inputs')
+        network, _ = build_cnn(input_var)
+        network = DenseLayer(lasagne.layers.dropout(network, p=.5),
+                             num_units=256,
+                             nonlinearity=lasagne.nonlinearities.rectify)
+        network = DenseLayer(lasagne.layers.dropout(network, p=.5),
+                             num_units=num_classes,
+                             nonlinearity=lasagne.nonlinearities.softmax)
+    else:
+        raise ValueError("Model not supported ['1dconv', 'maxpool', 'lstm', 'mix', 'cnn']")
+    # Create a loss expression for training, i.e., a scalar objective we want
+    # to minimize (for our multi-class problem, it is the cross-entropy loss):
+    prediction = lasagne.layers.get_output(network)
+    loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
+    loss = loss.mean()
+    params = lasagne.layers.get_all_params(network, trainable=True)
+    updates = lasagne.updates.adam(loss, params, learning_rate=0.001)
+    # Create a loss expression for validation/testing. The crucial difference
+    # here is that we do a deterministic forward pass through the network,
+    # disabling dropout layers.
+    test_prediction = lasagne.layers.get_output(network, deterministic=True)
+    test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
+                                                            target_var)
+    test_loss = test_loss.mean()
+    # As a bonus, also create an expression for the classification accuracy:
+    test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
+                      dtype=theano.config.floatX)
+    # Compile a function performing a training step on a mini-batch (by giving
+    # the updates dictionary) and returning the corresponding training loss:
+    train_fn = theano.function([input_var, target_var], loss, updates=updates)
+    # Compile a second function computing the validation loss and accuracy:
+    val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
+    # Finally, launch the training loop.
+    print("Starting training...")
+    best_validation_accu = 0
+    # We iterate over epochs:
+    for epoch in range(num_epochs):
+        # In each epoch, we do a full pass over the training data:
+        train_err = 0
+        train_batches = 0
+        start_time = time.time()
+        for batch in iterate_minibatches(X_train, y_train, batch_size, shuffle=False):
+            inputs, targets = batch
+            train_err += train_fn(inputs, targets)
+            train_batches += 1
+        # And a full pass over the validation data:
+        val_err = 0
+        val_acc = 0
+        val_batches = 0
+        for batch in iterate_minibatches(X_val, y_val, batch_size, shuffle=False):
+            inputs, targets = batch
+            err, acc = val_fn(inputs, targets)
+            val_err += err
+            val_acc += acc
+            val_batches += 1
+        av_train_err = train_err / train_batches
+        av_val_err = val_err / val_batches
+        av_val_acc = val_acc / val_batches
+        # Then we print the results for this epoch:
+        print("Epoch {} of {} took {:.3f}s".format(
+            epoch + 1, num_epochs, time.time() - start_time))
+        print("  training loss:\t\t{:.6f}".format(av_train_err))
+        print("  validation loss:\t\t{:.6f}".format(av_val_err))
+        print("  validation accuracy:\t\t{:.2f} %".format(av_val_acc * 100))
+        if av_val_acc > best_validation_accu:
+            best_validation_accu = av_val_acc
+            # After training, we compute and print the test error:
+            test_err = 0
+            test_acc = 0
+            test_batches = 0
+            for batch in iterate_minibatches(X_test, y_test, batch_size, shuffle=False):
+                inputs, targets = batch
+                err, acc = val_fn(inputs, targets)
+                test_err += err
+                test_acc += acc
+                test_batches += 1
+            av_test_err = test_err / test_batches
+            av_test_acc = test_acc / test_batches
+            print("Final results:")
+            print("  test loss:\t\t\t{:.6f}".format(av_test_err))
+            print("  test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))
+            # Dump the network weights to a file like this:
+            np.savez('weights_lasg_{0}'.format(model_type), *lasagne.layers.get_all_param_values(network))
+    print('-'*50)
+    print("Best validation accuracy:\t\t{:.2f} %".format(best_validation_accu * 100))
+    print("Best test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))
+
+'''
+if __name__ == '__main__':
+    from utils import reformatInput
+
+    # Load electrode locations
+    print('Loading data...')
+    locs = scipy.io.loadmat('../Sample data/Neuroscan_locs_orig.mat')
+    locs_3d = locs['A']
+    locs_2d = []
+    # Convert to 2D
+    for e in locs_3d:
+        locs_2d.append(azim_proj(e))
+
+    feats = scipy.io.loadmat('../Sample data/FeatureMat_timeWin.mat')['features']
+    print ('Feats Shape: ',feats.shape)
+    subj_nums = np.squeeze(scipy.io.loadmat('../Sample data/trials_subNums.mat')['subjectNum'])
+    # Leave-Subject-Out cross validation
+    fold_pairs = []
+    for i in np.unique(subj_nums):
+        ts = subj_nums == i
+        tr = np.squeeze(np.nonzero(np.bitwise_not(ts)))
+        ts = np.squeeze(np.nonzero(ts))
+        np.random.shuffle(tr)  # Shuffle indices
+        np.random.shuffle(ts)
+        fold_pairs.append((tr, ts))
+
+    # CNN Mode
+    print('Generating images...')
+    # Find the average response over time windows
+    av_feats = reduce(lambda x, y: x+y, [feats[:, i*192:(i+1)*192] for i in range(feats.shape[1] / 192)])
+    av_feats = av_feats / (feats.shape[1] / 192)
+    images = gen_images(np.array(locs_2d),
+                                  av_feats,
+                                  32, normalize=False)
+    print('\n')
+
+    # Class labels should start from 0
+    print('Training the CNN Model...')
+    train(images, np.squeeze(feats[:, -1]) - 1, fold_pairs[2], 'cnn')
+
+    # Conv-LSTM Mode
+    print('Generating images for all time windows...')
+    images_timewin = np.array([gen_images(np.array(locs_2d),
+                                                    feats[:, i * 192:(i + 1) * 192], 32, normalize=False) for i in
+                                         range(feats.shape[1] / 192)
+                                         ])
+    print('\n')
+    print('Training the LSTM-CONV Model...')
+    train(images_timewin, np.squeeze(feats[:, -1]) - 1, fold_pairs[2], 'mix')
+
+    print('Done!')
+'''