import Tester
import json
import time
import numpy as np

class BayesMatrixFactorTester(Tester.Tester):

    def __init__(self, details):
        super(BayesMatrixFactorTester, self).__init__(details)

        tests_file = details["tests"]

        with open(tests_file, "r") as data_file:
            self.__tests = json.load(data_file)
        self.__break_on_first_error = details["break on first error"]
        self.__break_on_first_wrong = details["break on first wrong"]
        self.__default_timeout = details.get("default timeout") or 5.0
        pass


    def test(self, project_name, submission):

        tests = self.__tests

        for data in tests:
            timeout = self.__default_timeout

            inputparams = data["input"]
            timeout = data.get("timeout") or self.__default_timeout

            pr = inputparams.split(",")

            I = int(pr[0])
            J = int(pr[1])
            L = int(pr[2])
            beta = float(pr[3])
            RMSE_max = float(pr[4])

            # Create target matrix
            R = np.random.rand(I, J)

            input = '{0},{1},{2},{3:.1f}\n'.format(I, J, L, beta)
            input_first_row = input
            input += '\n'.join([','.join(['{0:.3f}'.format(j) for j in R[i, :]]) for i in range(I)])


            (stdout, stderr, extraerr) = submission.run(project_name, input, timeout = timeout)

            stdout = stdout.decode('utf-8','ignore').encode('ascii','replace').strip()

            eval_inputs = {}
            eval_inputs["R"] = R
            eval_inputs["RMSE_max"] = RMSE_max
            eval_inputs["I"] = I
            eval_inputs["J"] = J
            eval_inputs["L"] = L
            eval_inputs["beta"] = beta


            if not (stderr or extraerr):
                (result, message) = self.evaluator.evaluate(eval_inputs, None, stdout, submission.log)
            else:
                result = 0
                if stderr:
                    message = "Runtime error:\n%s\n\nfor input:\n%s" % (stderr,input_first_row)
                else:
                    message = extraerr + '\n first 10000 chars of stdout was:' + stdout[0:min(10000,len(stdout))]

            submission.log.log_test(project_name, eval_inputs, "", stdout, result, message)

            if self.__break_on_first_error and (stderr or extraerr):
                break

            if self.__break_on_first_wrong and result != 1.0:
                break
fb2575e9ac87ad713a19f24bf6fb88d9c0cf4438
<mask token>


class BayesMatrixFactorTester(Tester.Tester):
    <mask token>
    <mask token>

<mask token>


class BayesMatrixFactorTester(Tester.Tester):
    <mask token>

    def test(self, project_name, submission):
        tests = self.__tests
        for data in tests:
            timeout = self.__default_timeout
            inputparams = data['input']
            timeout = data.get('timeout') or self.__default_timeout
            pr = inputparams.split(',')
            I = int(pr[0])
            J = int(pr[1])
            L = int(pr[2])
            beta = float(pr[3])
            RMSE_max = float(pr[4])
            R = np.random.rand(I, J)
            input = '{0},{1},{2},{3:.1f}\n'.format(I, J, L, beta)
            input_first_row = input
            input += '\n'.join([','.join(['{0:.3f}'.format(j) for j in R[i,
                :]]) for i in range(I)])
            stdout, stderr, extraerr = submission.run(project_name, input,
                timeout=timeout)
            stdout = stdout.decode('utf-8', 'ignore').encode('ascii', 'replace'
                ).strip()
            eval_inputs = {}
            eval_inputs['R'] = R
            eval_inputs['RMSE_max'] = RMSE_max
            eval_inputs['I'] = I
            eval_inputs['J'] = J
            eval_inputs['L'] = L
            eval_inputs['beta'] = beta
            if not (stderr or extraerr):
                result, message = self.evaluator.evaluate(eval_inputs, None,
                    stdout, submission.log)
            else:
                result = 0
                if stderr:
                    message = 'Runtime error:\n%s\n\nfor input:\n%s' % (stderr,
                        input_first_row)
                else:
                    message = (extraerr +
                        '\n first 10000 chars of stdout was:' + stdout[0:
                        min(10000, len(stdout))])
            submission.log.log_test(project_name, eval_inputs, '', stdout,
                result, message)
            if self.__break_on_first_error and (stderr or extraerr):
                break
            if self.__break_on_first_wrong and result != 1.0:
                break

<mask token>


class BayesMatrixFactorTester(Tester.Tester):

    def __init__(self, details):
        super(BayesMatrixFactorTester, self).__init__(details)
        tests_file = details['tests']
        with open(tests_file, 'r') as data_file:
            self.__tests = json.load(data_file)
        self.__break_on_first_error = details['break on first error']
        self.__break_on_first_wrong = details['break on first wrong']
        self.__default_timeout = details.get('default timeout') or 5.0
        pass

    def test(self, project_name, submission):
        tests = self.__tests
        for data in tests:
            timeout = self.__default_timeout
            inputparams = data['input']
            timeout = data.get('timeout') or self.__default_timeout
            pr = inputparams.split(',')
            I = int(pr[0])
            J = int(pr[1])
            L = int(pr[2])
            beta = float(pr[3])
            RMSE_max = float(pr[4])
            R = np.random.rand(I, J)
            input = '{0},{1},{2},{3:.1f}\n'.format(I, J, L, beta)
            input_first_row = input
            input += '\n'.join([','.join(['{0:.3f}'.format(j) for j in R[i,
                :]]) for i in range(I)])
            stdout, stderr, extraerr = submission.run(project_name, input,
                timeout=timeout)
            stdout = stdout.decode('utf-8', 'ignore').encode('ascii', 'replace'
                ).strip()
            eval_inputs = {}
            eval_inputs['R'] = R
            eval_inputs['RMSE_max'] = RMSE_max
            eval_inputs['I'] = I
            eval_inputs['J'] = J
            eval_inputs['L'] = L
            eval_inputs['beta'] = beta
            if not (stderr or extraerr):
                result, message = self.evaluator.evaluate(eval_inputs, None,
                    stdout, submission.log)
            else:
                result = 0
                if stderr:
                    message = 'Runtime error:\n%s\n\nfor input:\n%s' % (stderr,
                        input_first_row)
                else:
                    message = (extraerr +
                        '\n first 10000 chars of stdout was:' + stdout[0:
                        min(10000, len(stdout))])
            submission.log.log_test(project_name, eval_inputs, '', stdout,
                result, message)
            if self.__break_on_first_error and (stderr or extraerr):
                break
            if self.__break_on_first_wrong and result != 1.0:
                break

import Tester
import json
import time
import numpy as np


class BayesMatrixFactorTester(Tester.Tester):

    def __init__(self, details):
        super(BayesMatrixFactorTester, self).__init__(details)
        tests_file = details['tests']
        with open(tests_file, 'r') as data_file:
            self.__tests = json.load(data_file)
        self.__break_on_first_error = details['break on first error']
        self.__break_on_first_wrong = details['break on first wrong']
        self.__default_timeout = details.get('default timeout') or 5.0
        pass

    def test(self, project_name, submission):
        tests = self.__tests
        for data in tests:
            timeout = self.__default_timeout
            inputparams = data['input']
            timeout = data.get('timeout') or self.__default_timeout
            pr = inputparams.split(',')
            I = int(pr[0])
            J = int(pr[1])
            L = int(pr[2])
            beta = float(pr[3])
            RMSE_max = float(pr[4])
            R = np.random.rand(I, J)
            input = '{0},{1},{2},{3:.1f}\n'.format(I, J, L, beta)
            input_first_row = input
            input += '\n'.join([','.join(['{0:.3f}'.format(j) for j in R[i,
                :]]) for i in range(I)])
            stdout, stderr, extraerr = submission.run(project_name, input,
                timeout=timeout)
            stdout = stdout.decode('utf-8', 'ignore').encode('ascii', 'replace'
                ).strip()
            eval_inputs = {}
            eval_inputs['R'] = R
            eval_inputs['RMSE_max'] = RMSE_max
            eval_inputs['I'] = I
            eval_inputs['J'] = J
            eval_inputs['L'] = L
            eval_inputs['beta'] = beta
            if not (stderr or extraerr):
                result, message = self.evaluator.evaluate(eval_inputs, None,
                    stdout, submission.log)
            else:
                result = 0
                if stderr:
                    message = 'Runtime error:\n%s\n\nfor input:\n%s' % (stderr,
                        input_first_row)
                else:
                    message = (extraerr +
                        '\n first 10000 chars of stdout was:' + stdout[0:
                        min(10000, len(stdout))])
            submission.log.log_test(project_name, eval_inputs, '', stdout,
                result, message)
            if self.__break_on_first_error and (stderr or extraerr):
                break
            if self.__break_on_first_wrong and result != 1.0:
                break

import Tester
import json
import time
import numpy as np

class BayesMatrixFactorTester(Tester.Tester):

    def __init__(self, details):
        super(BayesMatrixFactorTester, self).__init__(details)

        tests_file = details["tests"]

        with open(tests_file, "r") as data_file:
            self.__tests = json.load(data_file)
        self.__break_on_first_error = details["break on first error"]
        self.__break_on_first_wrong = details["break on first wrong"]
        self.__default_timeout = details.get("default timeout") or 5.0
        pass


    def test(self, project_name, submission):

        tests = self.__tests

        for data in tests:
            timeout = self.__default_timeout

            inputparams = data["input"]
            timeout = data.get("timeout") or self.__default_timeout

            pr = inputparams.split(",")

            I = int(pr[0])
            J = int(pr[1])
            L = int(pr[2])
            beta = float(pr[3])
            RMSE_max = float(pr[4])

            # Create target matrix
            R = np.random.rand(I, J)

            input = '{0},{1},{2},{3:.1f}\n'.format(I, J, L, beta)
            input_first_row = input
            input += '\n'.join([','.join(['{0:.3f}'.format(j) for j in R[i, :]]) for i in range(I)])


            (stdout, stderr, extraerr) = submission.run(project_name, input, timeout = timeout)

            stdout = stdout.decode('utf-8','ignore').encode('ascii','replace').strip()

            eval_inputs = {}
            eval_inputs["R"] = R
            eval_inputs["RMSE_max"] = RMSE_max
            eval_inputs["I"] = I
            eval_inputs["J"] = J
            eval_inputs["L"] = L
            eval_inputs["beta"] = beta


            if not (stderr or extraerr):
                (result, message) = self.evaluator.evaluate(eval_inputs, None, stdout, submission.log)
            else:
                result = 0
                if stderr:
                    message = "Runtime error:\n%s\n\nfor input:\n%s" % (stderr,input_first_row)
                else:
                    message = extraerr + '\n first 10000 chars of stdout was:' + stdout[0:min(10000,len(stdout))]

            submission.log.log_test(project_name, eval_inputs, "", stdout, result, message)

            if self.__break_on_first_error and (stderr or extraerr):
                break

            if self.__break_on_first_wrong and result != 1.0:
                break