import cv2
import numpy as np
from numpy import genfromtxt
from copy import copy

X = genfromtxt('data1', delimiter=',')
Y = genfromtxt('Y', delimiter=',')
Y = np.float32(Y)


print X[0, :]

sample_n, var_n = X.shape
var_types = np.array([cv2.CV_VAR_NUMERICAL] * var_n + [cv2.CV_VAR_ORDERED], np.float32)

model = cv2.RTrees()

params = dict(max_depth=10, max_num_of_trees_in_the_forest=10, cv_folds=5, regression_accuracy=0.0001)

features = ['PEF', 'FEV1', 'FVC', 'TIFF']

a = model

save_f = np.zeros((np.shape(X)[0], np.shape(X)[1]/len(features)))
save_all = {}

for r in xrange(0, len(features)):
    for j in xrange(0, np.shape(X)[0]):
        u = 0
        for i in range(r, np.shape(X)[1], len(features)):
            save_f[j, u] = X[j, i]
            u += 1
    save_all[features[r]] = copy(np.float32(save_f))

print 'save_all'
for r in xrange(0, len(features)):
    print save_all[features[r]][0, :]

print X.dtype
print Y.dtype

#print Y


print save_all['PEF'].dtype

for i in range(0, len(features)):
    print 'Starting: ' + features[i]

    #print (X[train,:][:, [0,4,8,12,16,20]])
    print 'one cycle'
    print save_all[features[i]]
    print (Y[:][:, 0])
    print save_all[features[i]].shape
    print (Y[:][:, 0]).shape
    a.train(save_all[features[i]], cv2.CV_ROW_SAMPLE, Y[:][:, i], params=params, varType=var_types)