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How to use _min method in yandex-tank

Best Python code snippet using yandex-tank

find_path_turtle.py

Source:find_path_turtle.py

1import random2def grid_make():3    """4    A test function to generate a random grid.5    """6    grid = []7    size_x = 10008    size_y = 10009    gap = 1510    for x in range(size_x//gap):11        row = []12        for y in range(size_y//gap):13            r = random.random()14            if r <= .7:15                row.append(0)16            else:17                row.append(3)18        grid.append(row)19    indexes = [x for x in range(size_x//gap)]20    rs = indexes[random.randrange(0, len(indexes))]21    indexes.remove(rs)22    cs = indexes[random.randrange(0, len(indexes))]23    indexes.remove(cs)24    rt = indexes[random.randrange(0, len(indexes))]25    indexes.remove(rt)26    ct = indexes[random.randrange(0, len(indexes))]27    indexes.remove(ct)28    grid[rs][cs] = 129    grid[rt][ct] = 230    # for x in grid:31    #     print(x)32    return grid33def establish_knowns(grid):34    """35    Function to return dictionary that has the walls, start, and stop locations.36    """37    d = {'w':set()}38    for x in range(len(grid)):39        for y in range(len(grid[x])):40            if grid[x][y] == 1:41                d["s"] = (x, y)42            elif grid[x][y] == 2:43                d["t"] = (x, y)44            elif grid[x][y] == 3:45                d["w"].add((x, y))46    return d47# class that contains a variable to help the find_path function know which "node" to prioritize.48class Node:49    def __init__(self, g, loc):50        self.g = g51        self.loc = loc52# returns the euclidean distance between two points53def calc(pA, pB):54    """returns the euclidean distance between two points"""55    return (pA[0]-pB[0])**2+(pA[1]-pB[1])**256# needs optimizations badly. VERY BADLY57def find_path(loc, path, been_to, d, grid, paths):58    """59    Recursively returns the paths to target location borrowing a simple heuristic from popular path finding algorithms which is the euclidean distance. 60    This function isn't the best or efficient but it works and displays a fun gui.61    """62    if len(paths) == 0 and loc == d['t'] and tuple(path) not in paths:63        path.append(loc)64        paths.add(tuple(path))65        return True66    elif len(paths) > 0 and len(min(paths, key=len)) > len(path) and loc == d['t'] and tuple(path) not in paths:67        # print(len(path), len(paths))68        path.append(loc)69        paths.add(tuple(path))70        return True71    elif len(paths) == 0 or len(path) < len(min(paths, key=len)) and tuple(path) not in paths:72        been_to.add(loc)73        path.append(loc)74        options = []75        if loc[0] + 1 < len(grid) and 0 <= loc[1] < len(grid[loc[0]]) and (loc[0]+1, loc[1]) not in been_to and (loc[0]+1, loc[1]) not in d['w']:76            n = Node(calc((loc[0]+1, loc[1]), d['t']), (loc[0]+1, loc[1]))77            options.append(n)78        if loc[0] - 1 >= 0 and 0 <= loc[1] < len(grid[loc[0]]) and (loc[0] - 1, loc[1]) not in been_to and (loc[0]-1, loc[1]) not in d['w']:79            n = Node(calc((loc[0]-1, loc[1]), d['t']), (loc[0]-1, loc[1]))80            options.append(n)81        if loc[1] + 1 < len(grid[0]) and 0 <= loc[0] < len(grid) and (loc[0], loc[1] + 1) not in been_to and (loc[0], loc[1] + 1) not in d['w']:82            n = Node(calc((loc[0], loc[1]+1), d['t']), (loc[0], loc[1]+1))83            options.append(n)84        if loc[1] - 1 >= 0 and 0 <= loc[0] < len(grid) and (loc[0], loc[1] - 1) not in been_to and (loc[0], loc[1] - 1) not in d['w']:85            n = Node(calc((loc[0], loc[1]-1), d['t']), (loc[0], loc[1]-1))86            options.append(n)87        if len(options) == 0 or (len(paths) > 0 and len(path) > len(min(paths, key=len))) or tuple(path) in paths:88            return False89        options.sort(key=lambda x: x.g)90        # for x in options:91        #     print(x.g)92        # print('--------------')93        # print(len(options))94        for x in options:95            been_to_copy = been_to.copy()96            find_path(x.loc, path[:], been_to_copy, d, grid, paths)97class vertex:98    def __init__(self, weight, loc):99        self.weight = weight100        self.loc = loc101        self.visited = False102def Dijkstras(grid, info):103    nodes = [ [ vertex(float('inf'), (r, c)) for c in range(len(grid[r])) ] for r in range(len(grid)) ]104    path = {}105    vertexes = []106    for _ in nodes:107        for v in _:108            path[v.loc] = []109            vertexes.append(v)110    vertexes[(info['s'][0])*len(grid)+(info['s'][1])].weight = 0111    # print("start", vertexes[(info['s'][0])*len(grid)+(info['s'][1])].loc)112    # print("length of list", len(vertexes))113    while True:114        # print(list(map(lambda x: x.loc, path)))115        _min = vertex(float('inf'), None)116        i = 0117        for v in range(len(vertexes)):118            if _min.weight > vertexes[v].weight:119                _min = vertexes[v]120                i = v121        122        123        if (_min.loc == info['t']):124            print("DONE")125            break126        if not (_min.loc[0] + 1 >= len(grid)) and not vertexes[(_min.loc[0] + 1)*len(grid) + (_min.loc[1])].visited and vertexes[(_min.loc[0] + 1)*len(grid) + (_min.loc[1])].loc not in info['w']:127            # print(1, vertexes[(_min.loc[0] + 1)*len(grid) + (_min.loc[1])].loc)128            path[vertexes[(_min.loc[0] + 1)*len(grid) + (_min.loc[1])].loc].append(_min.loc)129            vertexes[(_min.loc[0] + 1)*len(grid) + (_min.loc[1])].weight = min(vertexes[(_min.loc[0] + 1)*len(grid) + (_min.loc[1])].weight, _min.weight + calc(info['s'], (_min.loc[0] + 1, _min.loc[1])))130        if not (_min.loc[0] - 1 < 0) and not vertexes[(_min.loc[0] - 1)*len(grid) + (_min.loc[1])].visited and vertexes[(_min.loc[0] - 1)*len(grid) + (_min.loc[1])].loc not in info['w']:131            # print(2, vertexes[(_min.loc[0] - 1)*len(grid) + (_min.loc[1])].loc)132            path[vertexes[(_min.loc[0] - 1)*len(grid) + (_min.loc[1])].loc].append(_min.loc)133            vertexes[(_min.loc[0] - 1)*len(grid) + (_min.loc[1])].weight = min(vertexes[(_min.loc[0] - 1)*len(grid) + (_min.loc[1])].weight, _min.weight + calc(info['s'], (_min.loc[0] - 1, _min.loc[1])))134        if not (_min.loc[1] + 1 >= len(grid[0])) and not vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] + 1)].visited and vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] + 1)].loc not in info['w']:135            # print(3, vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] + 1)].loc)136            path[vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] + 1)].loc].append(_min.loc)137            vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] + 1)].weight = min(vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] + 1)].weight, _min.weight + calc(info['s'], (_min.loc[0], _min.loc[1] + 1)))138        139        if not (_min.loc[1] - 1 < 0) and not vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] - 1)].visited and vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] - 1)].loc not in info['w']:140            # print(4, vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] - 1)].loc)141            path[vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] - 1)].loc].append(_min.loc)142            vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] - 1)].weight = min(vertexes[(_min.loc[0])*len(grid) + (_min.loc[1] - 1)].weight, _min.weight + calc(info['s'], (_min.loc[0], _min.loc[1] - 1)))143        vertexes[i].weight = float('inf')144        vertexes[i].visited = True145    journey = [info['t']]146    curr = path[info['t']][0]147    while True:148        if curr == info['s']:149            journey.append(curr)150            break151        journey.append(curr)152        curr = path[curr][0]153    return journey[::-1]154        155            156    157if __name__ == "__main__":158    grid = grid_make()159    print(len(grid), len(grid[0]))160    for x in grid:161        print(x)162    d = establish_knowns(grid)163    print("start: ", d['s'], "end: ", d['t'])164    # paths = set()165    # been_to = set()166    # find_path(d['s'], [], been_to, d, grid, paths)167    paths = Dijkstras(grid, d)...

minmax_normalization.py

Source:minmax_normalization.py

1"""2    MinMaxNormalization3"""4from __future__ import print_function5import numpy as np6np.random.seed(1337)  # for reproducibility789class MinMaxNormalization(object):10    '''MinMax Normalization --> [-1, 1]11       x = (x - min) / (max - min).12       x = x * 2 - 113    '''1415    def __init__(self):16        pass1718    def fit(self, X):19        self._min = X.min()20        self._max = X.max()21        print("min:", self._min, "max:", self._max)2223    def transform(self, X):24        X = 1. * (X - self._min) / (self._max - self._min)25        X = X * 2. - 1.26        return X2728    def fit_transform(self, X):29        self.fit(X)30        return self.transform(X)3132    def inverse_transform(self, X):33        X = (X + 1.) / 2.34        X = 1. * X * (self._max - self._min) + self._min35        return X363738class MinMaxNormalization_01(object):39    '''MinMax Normalization --> [0, 1]40       x = (x - min) / (max - min).41    '''4243    def __init__(self):44        pass4546    def fit(self, X):47        self._min = X.min()48        self._max = X.max()49        print("min:", self._min, "max:", self._max)5051    def transform(self, X):52        X = 1. * (X - self._min) / (self._max - self._min)53        return X5455    def fit_transform(self, X):56        self.fit(X)57        return self.transform(X)5859    def inverse_transform(self, X):60        X = 1. * X * (self._max - self._min) + self._min
...

preprocessing.py

Source:preprocessing.py

1import numpy as np2class MinMaxNormalization01(object):3	def __init__(self, ):4		pass5	def fit(self, data):6		self._min = np.amin(data)7		self._max = np.amax(data)8		print("min: ", self._min, "max:", self._max)9	def transform(self, data):10		norm_data = 1. * (data - self._min) / (self._max - self._min)11		return norm_data12	def fit_transform(self, data):13		self.fit(data)14		return self.transform(data)15	def inverse_transform(self, data):16		inverse_norm_data = 1. * data * (self._max - self._min) + self._min17		return inverse_norm_data18	def real_loss(self, loss):19		# loss is rmse20		return loss*(self._max - self._min)21		#return real_loss22class MinMaxNormalization_neg_1_pos_1(object):23    def __init__(self):24        pass25    def fit(self, X):26        self._min = X.min()27        self._max = X.max()28        print("min:", self._min, "max:", self._max)29    def transform(self, X):30        X = 1. * (X - self._min) / (self._max - self._min)31        X = X * 2. - 1.32        return X33    def fit_transform(self, X):34        self.fit(X)35        return self.transform(X)36    def inverse_transform(self, X):37    	X = (X + 1.)/2.38        X = 1. * X * (self._max - self._min) + self._min39        return X40    def real_loss(self, loss):41    	# loss is rmse42    	return loss*(self._max - self._min)/2....

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