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How to use check_iteration method in autotest

Best Python code snippet using autotest_python

main.py

Source:main.py

1INCODE_MATRIX = [[10, -3, -2, -1],2                 [3, 10, -3, -2],3                 [2, 3, 10, -3],4                 [1, 2, 3, 10]]56INCODE_F_VECTOR = [31, -17, 49, -19]78# globalne zmienne do sprawdzania (ro)zbieÅ¼noÅci metody9divergence_counter = 010last_diff_check = 99999911check_iteration = 1121314def get_sq_matrix(n):15    """16    funkcja zwraca macierz o rozmiarze NxN utworzonÄ17    z danych wprowadzanych przez uÅ¼ytkownika1819    :param n: integer20    :return: list[n][n]21    """22    matrix = []23    print("WprowadÅº wspÃ³Åczynniki ukÅadu rÃ³wnaÅ: ")24    for i in range(1, n+1):25        row = []26        for j in range(1, n+1):27            prompt = "a" + str(i) + str(j) + ": "28            row.append(int(input(prompt)))29        matrix.append(row)30    return matrix313233def get_start_vector(n):34    """35    funkcja zwraca wektor wstepnego przybliÅ¼enia utworzony36    z podanych przez uÅ¼ytkownika danych3738    :param n: int39    :return: list[n]40    """41    vector = []42    print("WprowadÅº wektor wstÄpnych przybliÅ¼eÅ rozwiÄzania: ")43    for i in range(1, n+1):44        prompt = "x" + str(i) + ": "45        vector.append(int(input(prompt)))46    return vector474849def get_f_vector(n):50    """51    funkcja zwraca wektor f utworzony z podanych przez uÅ¼ytkownika danych5253    :param n: int54    :return: list[n]55    """56    vector = []57    print("WprowadÅº wektor f: ")58    for i in range(1, n + 1):59        prompt = "f" + str(i) + ": "60        vector.append(int(input(prompt)))61    return vector626364def define_size():65    """66    funkcja zwraca podany przez uÅ¼ytkownika rozmiar macierzy6768    :return: n69    """70    return int(input("Zdefiniuj rozmiar macierzy (NxN): "))717273def make_choice():74    """75    funkcja pozwala uÅ¼ytkownikowi wybraÄ czy chce skorzystaÄ76    z macierzy wpisanej do programu, czy podaÄ nowÄ samemu7778    :return: boolean79    """80    print("Czy chcesz uÅ¼yÄ wÅasnej macierzy? (T/N)")81    ans = ""82    while ans not in ["t", "n"]:83        ans = input().lower()84    return "t" in ans858687def display(vector, precision):88    """89    funkcja wyÅwietla wyniki dziaÅania programu9091    :param vector: list[n][n]92    :param precision: float93    :return: None94    """95    print(f"PrzybliÅ¼enie rozwiÄzania ukÅadu z precyzjÄ = {precision}")96    for i in range(len(vector)):97        print(f"x{i+1} = {round(vector[i], 5)}")9899100def divergence_check(prev, curr):101    """102    funkcja sprawdza czy metoda jest rozbieÅ¼na dla podanego ukÅadu rÃ³wnaÅ103104    :param prev:105    :param curr:106    :return:107    """108    global last_diff_check109    global divergence_counter110    global check_iteration111    divergence_list = []112    for i in range(len(curr)):113        divergence_list.append(abs(prev[i] - curr[i]))114    current_max_divergence = max(divergence_list)115    print("p", check_iteration, " = ", round(current_max_divergence, 10), sep="")116    check_iteration += 1117    if current_max_divergence <= last_diff_check:118        last_diff_check = current_max_divergence119        divergence_counter = 0120    else:121        last_diff_check = current_max_divergence122        divergence_counter += 1123    if divergence_counter >= 5:124        print("Metoda jest rozbieÅ¼na dla podanego ukÅadu rÃ³wnaÅ")125        exit()126127128def check_precision(prev, curr, acc):129    """130    funkcja sprawdza czy kolejna iteracja osiÄgnÄÅa wymagane przybliÅ¼enie131132    :param prev: list[n]133    :param curr: list[n]134    :param acc: float135    :return: boolean136    """137    divergence_check(prev, curr)138    results = []139    for i in range(len(curr)):140        results.append(abs(prev[i] - curr[i]) > acc)141    for element in results:142        if element:143            return True144    else:145        return False146147148def gauss_seidl(matrix, f_vector, x_vector, precision):149    """150    funkcja przybliÅ¼a z zadanÄ dokÅadnoÅciÄ rozwiÄzanie ukÅadu151    rÃ³wnaÅ na podstawie macierzy wspÃ³ÅczynnikÃ³w,152    wektora f oraz wektora wstÄpnych przybliÅ¼eÅ rozwiÄzania153154    :param matrix: list[n][n]155    :param f_vector: list[n]156    :param x_vector: list[n]157    :param precision: float158    :return: list[n]159    """160    counter = 1161    previous_vector = x_vector.copy()162    try:163        for i in range(len(x_vector)):164            if i == 0:165                frac_top = -sum([matrix[i][j] * x_vector[j] for j in range(i + 1, len(x_vector))]) + f_vector[i]166                x_vector[i] = frac_top / matrix[i][i]167            else:168                right_side = -sum([matrix[i][j] * x_vector[j] for j in range(0, i)])169                left_side = -sum([matrix[i][j] * x_vector[j] for j in range(i + 1, len(x_vector))]) + f_vector[i]170                x_vector[i] = (right_side - left_side) / matrix[i][i]171        while check_precision(previous_vector, x_vector, precision):172            previous_vector = x_vector.copy()173            for i in range(len(x_vector)):174                if i == 0:175                    frac_top = -sum([matrix[i][j] * x_vector[j] for j in range(i+1, len(x_vector))]) + f_vector[i]176                    x_vector[i] = frac_top / matrix[i][i]177                else:178                    right_side = -sum([matrix[i][j] * x_vector[j] for j in range(0, i)])179                    left_side = -sum([matrix[i][j] * x_vector[j] for j in range(i+1, len(x_vector))]) + f_vector[i]180                    x_vector[i] = (right_side - left_side)/matrix[i][i]181            counter += 1182    except ZeroDivisionError:183        print("Macierz zawiera zera na przekÄtnej, uruchom program jeszcze raz zamieniajÄc kolejnoÅÄ wierszy.")184        exit()185    print("Wykonano iteracji:", counter)186    return x_vector187188189def main():190    print("Program uÅ¼ywa metody Gaussa-Seidla do przybliÅ¼enia rozwiÄzania ukÅadu rÃ³wnaÅ z zadanÄ dokÅadnoÅciÄ.")191    if make_choice():192        matrix_size = define_size()193        matrix = get_sq_matrix(matrix_size)194        f_vector = get_f_vector(matrix_size)195        x_vector = get_start_vector(matrix_size)196    else:197        matrix = INCODE_MATRIX.copy()198        f_vector = INCODE_F_VECTOR.copy()199        x_vector = get_start_vector(len(matrix))200    precision = float(input("Zadaj dokÅadnoÅÄ: "))201    approximation = gauss_seidl(matrix, f_vector, x_vector, precision)202    display(approximation, precision)203204205if __name__ == '__main__':
...

inet.py

Source:inet.py

...68        logger.info("Loaded data from {}".format(data_file))69        return result70    def _scrape_html(self):71        for k, v in self.data.items():72            if self.check_iteration(v.get('iteration')):73                url = v['website']74                try:75                    v['html'] = self.html_scraper.scrape(url)76                    logger.info("Stored html for {}".format(url))77                except TypeError:78                    logger.warn("No html stored for {}".format(url))79                    v['html'] = None80    def _scrape_twitter_handles(self):81        for k, v in self.data.items():82            if self.check_iteration(v.get('iteration')):83                url = v['website']84                try:85                    for html in v.get('html'):86                        v['twitter_handles'] = self.html_scraper.twitter_handles(html)  # noqa87                    logger.info("Found {} twitter handles in {}"88                                .format(len(v.get('twitter_handles', [])), url))  # noqa89                except TypeError:90                    logger.warn("No twitter handles stored for {}".format(url))91                    v['twitter_handles'] = None92    def twitter_followers(self):93        for k, v in self.data.items():94            if self.check_iteration(v.get('iteration')):95                v['twitter_followers'] = self.twitter_handler.followers(v['twitter_handles'])  # noqa96    def twitter_following(self):97        for k, v in self.data.items():98            if self.check_iteration(v.get('iteration')):99                v['twitter_following'] = self.twitter_handler.following(v['twitter_handles'])  # noqa100    def mutual_follows(self):101        for k, v in self.data.items():102            v['mutual_follows'] = {}103            if self.check_iteration(v.get('iteration')):104                for handle in v['twitter_followers']:105                    followers = v['twitter_followers'][handle]106                    following = v['twitter_following'][handle]107                    mutual_set = set(followers).intersection(following)108                    mutual_list = list(mutual_set)109                v['mutual_follows'][handle] = mutual_list110    def _get_company_data(self):111        for k, v in self.data.items():112            if self.check_iteration(v.get('iteration')):113                v['company_data'] = self.ch_client.get_company_data(k, v)114    def _shared_directors(self):115        for k, v in self.data.items():116            if self.check_iteration(v.get('iteration')):117                self.data[k]['directors'] = self.ch_client.get_directors(k, v)118    def check_iteration(self, iteration):119        return iteration == self._iteration120    def start(self, iterations=1):121        """Start the iteration process.122        Starts the iteration process that expands the original seed123        data.124        Parameters125        ----------126        iterations: int, default 5127            Number of iterations to complete128        Returns129        -------130        None131        """132        logger.info("Starting data crawl. Number of iterations is {}"...

euler52.py

Source:euler52.py

1iteration_works = False2i = 03def check_iteration(i):4    stuff5while not iteration_works:6    i += 17    if check_iteration(i):8        iteration_works = True...

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