--- a
+++ b/model/NextXVisit.py
@@ -0,0 +1,139 @@
+import torch
+import torch.nn as nn
+import pytorch_pretrained_bert as Bert
+import numpy as np
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word, segment, age
+    """
+
+    def __init__(self, config, feature_dict=None):
+        super(BertEmbeddings, self).__init__()
+
+        if feature_dict is None:
+            self.feature_dict = {
+                'word': True,
+                'seg': True,
+                'age': True,
+                'position': True
+            }
+        else:
+            self.feature_dict = feature_dict
+
+        if feature_dict['word']:
+            self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+
+        if feature_dict['seg']:
+            self.segment_embeddings = nn.Embedding(config.seg_vocab_size, config.hidden_size)
+
+        if feature_dict['age']:
+            self.age_embeddings = nn.Embedding(config.age_vocab_size, config.hidden_size)
+
+        if feature_dict['position']:
+            self.posi_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size). \
+            from_pretrained(embeddings=self._init_posi_embedding(config.max_position_embeddings, config.hidden_size))
+
+        self.LayerNorm = Bert.modeling.BertLayerNorm(config.hidden_size, eps=1e-12)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, word_ids, age_ids, seg_ids, posi_ids):
+        embeddings = self.word_embeddings(word_ids)
+
+        if self.feature_dict['seg']:
+            segment_embed = self.segment_embeddings(seg_ids)
+            embeddings = embeddings + segment_embed
+
+        if self.feature_dict['age']:
+            age_embed = self.age_embeddings(age_ids)
+            embeddings = embeddings + age_embed
+
+        if self.feature_dict['position']:
+            posi_embeddings = self.posi_embeddings(posi_ids)
+            embeddings = embeddings + posi_embeddings
+
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+    def _init_posi_embedding(self, max_position_embedding, hidden_size):
+        def even_code(pos, idx):
+            return np.sin(pos / (10000 ** (2 * idx / hidden_size)))
+
+        def odd_code(pos, idx):
+            return np.cos(pos / (10000 ** (2 * idx / hidden_size)))
+
+        # initialize position embedding table
+        lookup_table = np.zeros((max_position_embedding, hidden_size), dtype=np.float32)
+
+        # reset table parameters with hard encoding
+        # set even dimension
+        for pos in range(max_position_embedding):
+            for idx in np.arange(0, hidden_size, step=2):
+                lookup_table[pos, idx] = even_code(pos, idx)
+        # set odd dimension
+        for pos in range(max_position_embedding):
+            for idx in np.arange(1, hidden_size, step=2):
+                lookup_table[pos, idx] = odd_code(pos, idx)
+
+        return torch.tensor(lookup_table)
+
+
+class BertModel(Bert.modeling.BertPreTrainedModel):
+    def __init__(self, config, feature_dict):
+        super(BertModel, self).__init__(config)
+        self.embeddings = BertEmbeddings(config=config, feature_dict=feature_dict)
+        self.encoder = Bert.modeling.BertEncoder(config=config)
+        self.pooler = Bert.modeling.BertPooler(config)
+        self.apply(self.init_bert_weights)
+
+    def forward(self, input_ids, age_ids, seg_ids, posi_ids, attention_mask,
+                output_all_encoded_layers=True):
+
+        # We create a 3D attention mask from a 2D tensor mask.
+        # Sizes are [batch_size, 1, 1, to_seq_length]
+        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+        # this attention mask is more simple than the triangular masking of causal attention
+        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+        embedding_output = self.embeddings(input_ids, age_ids, seg_ids, posi_ids)
+        encoded_layers = self.encoder(embedding_output,
+                                      extended_attention_mask,
+                                      output_all_encoded_layers=output_all_encoded_layers)
+        sequence_output = encoded_layers[-1]
+        pooled_output = self.pooler(sequence_output)
+        if not output_all_encoded_layers:
+            encoded_layers = encoded_layers[-1]
+        return encoded_layers, pooled_output
+
+
+class BertForMultiLabelPrediction(Bert.modeling.BertPreTrainedModel):
+    def __init__(self, config, num_labels, feature_dict):
+        super(BertForMultiLabelPrediction, self).__init__(config)
+        self.num_labels = num_labels
+        self.bert = BertModel(config, feature_dict)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.apply(self.init_bert_weights)
+
+    def forward(self, input_ids, age_ids=None, seg_ids=None, posi_ids=None, attention_mask=None, labels=None):
+        _, pooled_output = self.bert(input_ids, age_ids, seg_ids, posi_ids, attention_mask,
+                                     output_all_encoded_layers=False)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        if labels is not None:
+            loss_fct = nn.MultiLabelSoftMarginLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1, self.num_labels))
+            return loss, logits
+        else:
+            return logits
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