--- a
+++ b/kernel_classifier.py
@@ -0,0 +1,145 @@
+import json
+import csv
+import numpy as np
+import os
+import pandas as pd
+import random
+import tensorflow as tf
+
+from functions import quan_detector, most_repeared_promoter,dataset
+from sklearn.metrics import confusion_matrix
+
+from sklearn import datasets, linear_model,svm
+from sklearn.metrics import mean_squared_error, r2_score
+
+np.random.seed(42)
+tf.set_random_seed(42)
+random.seed(42)
+
+
+
+def get_input_fn(dataset_split, batch_size, capacity=10000, min_after_dequeue=3000):
+
+  def _input_fn():
+    images_batch, labels_batch = tf.train.shuffle_batch(
+        tensors=dataset_split,
+        batch_size=batch_size,
+        capacity=capacity,
+        min_after_dequeue=min_after_dequeue,
+        enqueue_many=True,
+        num_threads=4)
+    features_map = {'images': images_batch}
+    return features_map, labels_batch
+
+  return _input_fn
+
+out_put_header = ['Promoter region','Posotive_zeros','Negative_zeros','Sum_zeros',
+                  'Positive_freq', 'Negative_freq','Sum_freq',
+                  'Sum_all','Percent_all', 'Vector_freq',
+                  "AUC","Accuracy",
+                  '>50%']
+
+output_file_name = 'output_kernelC.csv'
+# with open(output_file_name,'w') as f:
+#     writer = csv.writer(f)
+#     writer.writerow(out_put_header)
+
+labels_file = 'labes.csv'
+labels_df = pd.read_csv(labels_file, index_col=0)
+ids_csv = labels_df.FID.tolist()
+
+
+promoters_list = range(100)
+for promoter_num in promoters_list:
+    print promoter_num
+    promoter_file = 'promoters/chr22_'+str(promoter_num)+'.json'
+    # # read files
+    with open(promoter_file) as json_data:
+        ind_var = json.load(json_data)
+    ids_json = ind_var.keys()
+
+    var_num = []
+    for i in ids_csv:
+        id_name = str(i)
+        temp = ind_var[id_name]
+        var_seq = map(int, temp)
+        var_num.append(var_seq)
+
+    labels_df['vars'] = var_num
+    lab_num = {1: [1,0], # positive
+               2: [0,1]} # negative
+
+    pheno_new = []
+    for i in labels_df.Pheno.tolist():
+        pheno_new.append(lab_num[i])
+    d = {"Pheno": pheno_new, "Vars":labels_df.vars}
+    dataset_ = pd.DataFrame(d)
+
+    dataset_X = np.array(dataset_.Vars.tolist(),dtype=np.float32)
+    dataset_Y = np.array(dataset_.Pheno.tolist(),dtype=np.float32)
+
+    N = len(dataset_X)
+
+    # repeat information
+    per_zeros, p_zeros,n_zeros = quan_detector(dataset_X,dataset_Y)
+    count_zeros = p_zeros+n_zeros # sum of individuals without any variants
+
+    most_vector, max_count,count_vector = most_repeared_promoter(dataset_X,dataset_Y)
+    _, p_count,n_count = count_vector
+
+    vart_pos = []
+    for i in range(len(most_vector)):
+        if most_vector[i] != '0.0':
+            vart_pos.append(i)
+
+    np.random.seed(42)
+    tf.set_random_seed(42)
+    random.seed(42)
+
+    # network accuracy
+
+    x_train, y_train,x_test,y_test = dataset(dataset_X,dataset_Y,test_ratio=0.1)
+    y_train = np.argmax(y_train, axis=1)
+    y_test = np.argmax(y_test, axis=1)
+
+    data = {}
+    data['train'] = [x_train, y_train]
+    data['test'] = [x_test, y_test]
+
+
+    train_input_fn = get_input_fn(data['train'], batch_size=256)
+    eval_input_fn = get_input_fn(data['test'], batch_size=len(y_test))
+
+    image_column = tf.contrib.layers.real_valued_column('images', dimension=64)
+    optimizer = tf.train.FtrlOptimizer(
+        learning_rate=50.0, l2_regularization_strength=0.001)
+
+    kernel_mapper = tf.contrib.kernel_methods.RandomFourierFeatureMapper(
+        input_dim=64, output_dim=2000, stddev=5.0, name='rffm')
+    kernel_mappers = {image_column: [kernel_mapper]}
+    estimator = tf.contrib.kernel_methods.KernelLinearClassifier(
+        n_classes=2, optimizer=optimizer, kernel_mappers=kernel_mappers)
+
+    estimator.fit(input_fn=train_input_fn, steps=2000)
+    eval_metrics = estimator.evaluate(input_fn=eval_input_fn, steps=1)
+    # print(eval_metrics.items())
+    # # Make predictions using the testing set
+    # y_pred = estimator.predict(input_fn=eval_input_fn)
+    # # y_pred = np.argmax(y_pred,axis=1)
+    # y_test_num = y_test
+    # tn, fp, fn, tp = confusion_matrix(y_test_num, y_pred).ravel()
+
+    acc = eval_metrics['accuracy']
+    auc = eval_metrics['auc_precision_recall']
+
+
+    info = ['promoter '+str(promoter_num), p_zeros,n_zeros,count_zeros,
+            p_count, n_count, max_count,
+            max_count + count_zeros, (max_count + count_zeros)*1./N, vart_pos,
+            auc, acc, acc>0.5]
+
+
+    with open(output_file_name,'a') as f:
+        writer = csv.writer(f)
+        writer.writerow(info)
+print "Done"
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