# coding: utf-8

import threading
from MachineLearningModule import machine_learn
from flask import Flask,request

class machineLearnThread (threading.Thread):
	def __init__(self,loss,n_estimators,max_depth,max_features,
		learning_rate,min_samples_split,min_samples_leaf,subsample):
		self.loss = loss
		self.n_estimators=n_estimators
		self.max_depth = max_depth
		self.max_features =  max_features
		self.learning_rate = learning_rate
		self.min_samples_split = min_samples_split
		self.min_samples_leaf = min_samples_leaf
		self.subsample=subsample
		threading.Thread.__init__(self)
	def run(self):
		machine_learn.learn(self.loss,self.n_estimators,self.max_depth,self.max_features,
			self.learning_rate,self.min_samples_split,self.min_samples_leaf,self.subsample)

app = Flask(__name__)


@app.route("/machinelearn",methods=["GET"])
def machinelearn():
	loss = 'ls'
	n_estimators=200
	max_depth = 8
	max_features =  'sqrt' 
	learning_rate = 0.05
	min_samples_split = 500
	min_samples_leaf = 50
	subsample=0.8
	thread1 = machineLearnThread(loss,n_estimators,max_depth,max_features,
    	learning_rate,min_samples_split,min_samples_leaf,subsample)
	thread1.start()
	return 'Learning!!'



@app.route("/predict",methods=["PUT"])
def predict():
	if request.is_json:
		predict_params = request.json
		print(len(predict_params['current_positions']))
		if(len(predict_params['current_positions'])==56):
			recomended_position = machine_learn.find_position(predict_params['current_positions'],predict_params['item_type'],10);
			return str(recomended_position),200
		else:
			return 'The Size of the Recomended Positions must be 56',400
	else:
		return 'The Body MUST BE A JSON',400
	

app.run(debug=True, use_reloader=True)
3152a356f26a696ba97d2d5f17f1b2c7da0cad20
<mask token>


class machineLearnThread(threading.Thread):

    def __init__(self, loss, n_estimators, max_depth, max_features,
        learning_rate, min_samples_split, min_samples_leaf, subsample):
        self.loss = loss
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.max_features = max_features
        self.learning_rate = learning_rate
        self.min_samples_split = min_samples_split
        self.min_samples_leaf = min_samples_leaf
        self.subsample = subsample
        threading.Thread.__init__(self)

    def run(self):
        machine_learn.learn(self.loss, self.n_estimators, self.max_depth,
            self.max_features, self.learning_rate, self.min_samples_split,
            self.min_samples_leaf, self.subsample)


<mask token>


@app.route('/machinelearn', methods=['GET'])
def machinelearn():
    loss = 'ls'
    n_estimators = 200
    max_depth = 8
    max_features = 'sqrt'
    learning_rate = 0.05
    min_samples_split = 500
    min_samples_leaf = 50
    subsample = 0.8
    thread1 = machineLearnThread(loss, n_estimators, max_depth,
        max_features, learning_rate, min_samples_split, min_samples_leaf,
        subsample)
    thread1.start()
    return 'Learning!!'


@app.route('/predict', methods=['PUT'])
def predict():
    if request.is_json:
        predict_params = request.json
        print(len(predict_params['current_positions']))
        if len(predict_params['current_positions']) == 56:
            recomended_position = machine_learn.find_position(predict_params
                ['current_positions'], predict_params['item_type'], 10)
            return str(recomended_position), 200
        else:
            return 'The Size of the Recomended Positions must be 56', 400
    else:
        return 'The Body MUST BE A JSON', 400


<mask token>

<mask token>


class machineLearnThread(threading.Thread):

    def __init__(self, loss, n_estimators, max_depth, max_features,
        learning_rate, min_samples_split, min_samples_leaf, subsample):
        self.loss = loss
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.max_features = max_features
        self.learning_rate = learning_rate
        self.min_samples_split = min_samples_split
        self.min_samples_leaf = min_samples_leaf
        self.subsample = subsample
        threading.Thread.__init__(self)

    def run(self):
        machine_learn.learn(self.loss, self.n_estimators, self.max_depth,
            self.max_features, self.learning_rate, self.min_samples_split,
            self.min_samples_leaf, self.subsample)


<mask token>


@app.route('/machinelearn', methods=['GET'])
def machinelearn():
    loss = 'ls'
    n_estimators = 200
    max_depth = 8
    max_features = 'sqrt'
    learning_rate = 0.05
    min_samples_split = 500
    min_samples_leaf = 50
    subsample = 0.8
    thread1 = machineLearnThread(loss, n_estimators, max_depth,
        max_features, learning_rate, min_samples_split, min_samples_leaf,
        subsample)
    thread1.start()
    return 'Learning!!'


@app.route('/predict', methods=['PUT'])
def predict():
    if request.is_json:
        predict_params = request.json
        print(len(predict_params['current_positions']))
        if len(predict_params['current_positions']) == 56:
            recomended_position = machine_learn.find_position(predict_params
                ['current_positions'], predict_params['item_type'], 10)
            return str(recomended_position), 200
        else:
            return 'The Size of the Recomended Positions must be 56', 400
    else:
        return 'The Body MUST BE A JSON', 400


app.run(debug=True, use_reloader=True)

<mask token>


class machineLearnThread(threading.Thread):

    def __init__(self, loss, n_estimators, max_depth, max_features,
        learning_rate, min_samples_split, min_samples_leaf, subsample):
        self.loss = loss
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.max_features = max_features
        self.learning_rate = learning_rate
        self.min_samples_split = min_samples_split
        self.min_samples_leaf = min_samples_leaf
        self.subsample = subsample
        threading.Thread.__init__(self)

    def run(self):
        machine_learn.learn(self.loss, self.n_estimators, self.max_depth,
            self.max_features, self.learning_rate, self.min_samples_split,
            self.min_samples_leaf, self.subsample)


app = Flask(__name__)


@app.route('/machinelearn', methods=['GET'])
def machinelearn():
    loss = 'ls'
    n_estimators = 200
    max_depth = 8
    max_features = 'sqrt'
    learning_rate = 0.05
    min_samples_split = 500
    min_samples_leaf = 50
    subsample = 0.8
    thread1 = machineLearnThread(loss, n_estimators, max_depth,
        max_features, learning_rate, min_samples_split, min_samples_leaf,
        subsample)
    thread1.start()
    return 'Learning!!'


@app.route('/predict', methods=['PUT'])
def predict():
    if request.is_json:
        predict_params = request.json
        print(len(predict_params['current_positions']))
        if len(predict_params['current_positions']) == 56:
            recomended_position = machine_learn.find_position(predict_params
                ['current_positions'], predict_params['item_type'], 10)
            return str(recomended_position), 200
        else:
            return 'The Size of the Recomended Positions must be 56', 400
    else:
        return 'The Body MUST BE A JSON', 400


app.run(debug=True, use_reloader=True)

import threading
from MachineLearningModule import machine_learn
from flask import Flask, request


class machineLearnThread(threading.Thread):

    def __init__(self, loss, n_estimators, max_depth, max_features,
        learning_rate, min_samples_split, min_samples_leaf, subsample):
        self.loss = loss
        self.n_estimators = n_estimators
        self.max_depth = max_depth
        self.max_features = max_features
        self.learning_rate = learning_rate
        self.min_samples_split = min_samples_split
        self.min_samples_leaf = min_samples_leaf
        self.subsample = subsample
        threading.Thread.__init__(self)

    def run(self):
        machine_learn.learn(self.loss, self.n_estimators, self.max_depth,
            self.max_features, self.learning_rate, self.min_samples_split,
            self.min_samples_leaf, self.subsample)


app = Flask(__name__)


@app.route('/machinelearn', methods=['GET'])
def machinelearn():
    loss = 'ls'
    n_estimators = 200
    max_depth = 8
    max_features = 'sqrt'
    learning_rate = 0.05
    min_samples_split = 500
    min_samples_leaf = 50
    subsample = 0.8
    thread1 = machineLearnThread(loss, n_estimators, max_depth,
        max_features, learning_rate, min_samples_split, min_samples_leaf,
        subsample)
    thread1.start()
    return 'Learning!!'


@app.route('/predict', methods=['PUT'])
def predict():
    if request.is_json:
        predict_params = request.json
        print(len(predict_params['current_positions']))
        if len(predict_params['current_positions']) == 56:
            recomended_position = machine_learn.find_position(predict_params
                ['current_positions'], predict_params['item_type'], 10)
            return str(recomended_position), 200
        else:
            return 'The Size of the Recomended Positions must be 56', 400
    else:
        return 'The Body MUST BE A JSON', 400


app.run(debug=True, use_reloader=True)

# coding: utf-8

import threading
from MachineLearningModule import machine_learn
from flask import Flask,request

class machineLearnThread (threading.Thread):
	def __init__(self,loss,n_estimators,max_depth,max_features,
		learning_rate,min_samples_split,min_samples_leaf,subsample):
		self.loss = loss
		self.n_estimators=n_estimators
		self.max_depth = max_depth
		self.max_features =  max_features
		self.learning_rate = learning_rate
		self.min_samples_split = min_samples_split
		self.min_samples_leaf = min_samples_leaf
		self.subsample=subsample
		threading.Thread.__init__(self)
	def run(self):
		machine_learn.learn(self.loss,self.n_estimators,self.max_depth,self.max_features,
			self.learning_rate,self.min_samples_split,self.min_samples_leaf,self.subsample)

app = Flask(__name__)


@app.route("/machinelearn",methods=["GET"])
def machinelearn():
	loss = 'ls'
	n_estimators=200
	max_depth = 8
	max_features =  'sqrt' 
	learning_rate = 0.05
	min_samples_split = 500
	min_samples_leaf = 50
	subsample=0.8
	thread1 = machineLearnThread(loss,n_estimators,max_depth,max_features,
    	learning_rate,min_samples_split,min_samples_leaf,subsample)
	thread1.start()
	return 'Learning!!'



@app.route("/predict",methods=["PUT"])
def predict():
	if request.is_json:
		predict_params = request.json
		print(len(predict_params['current_positions']))
		if(len(predict_params['current_positions'])==56):
			recomended_position = machine_learn.find_position(predict_params['current_positions'],predict_params['item_type'],10);
			return str(recomended_position),200
		else:
			return 'The Size of the Recomended Positions must be 56',400
	else:
		return 'The Body MUST BE A JSON',400
	

app.run(debug=True, use_reloader=True)