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How to use count_step method in hypothesis

Best Python code snippet using hypothesis

prediction.py

Source:prediction.py

1import numpy as np2class Prediction:3    def train(self, method_str, method, state, action, max_episodes, max_steps, file_log, state_kinds=None):4        episode_total_reward = []5        episode_step_reward = []6        episode_mae = []7        action_true = []8        action_predict = []9        for episode in range(max_episodes):10            episode_reward = 011            count_step = 012            index = np.random.choice(range(len(state)))13            s = state[index]14            if method_str == "DF-DQN":15                kind = state_kinds[index]16            for step in range(max_steps):17                count_step += 118                a_true = action[index]19                action_true.append(a_true)20                if method_str == "DQN":21                    a, a_value = method.choose_action(state=s, stage="train")22                    action_predict.append(a_value)23                    r = -abs(a_value - a_true)24                elif method_str == "DF-DQN":25                    a, a_value = method.choose_action(state=s, kind=kind, stage="train")26                    action_predict.append(a_value)27                    r = -abs(a_value - a_true)28                elif method_str == "DDPG":29                    a = method.choose_action(state=s, stage="train")30                    a = np.reshape(a, (1, -1))[0][0]31                    action_predict.append(a)32                    r = -abs(a - a_true)33                episode_step_reward.append(r)34                episode_reward += r35                index += 136                if index == len(state):37                    break38                s_ = state[index]39                if method_str == "DF-DQN":40                    kind = state_kinds[index]41                method.store_transition(s, a, r, s_)42                method.learn(count_step)43                if (index == len(state) - 1) or (step == max_steps - 1):44                    episode_total_reward.append(episode_reward)  # ä¿åæ¯ä¸ªååçç´¯è®¡å¥èµ45                    print('Episode %d : %.2f' % (episode, episode_reward))46                    file_log.write('Episode %d : %.2f\n' % (episode, episode_reward))  # æå°ååæ°åå¥èµç´¯è®¡å¼47                    break48                s = s_49            episode_reward = np.reshape(episode_reward, (1, -1))[0][0]50            episode_mae.append((-episode_reward) / count_step)  # è®¡ç®æ¯ååçmaeï¼çæ¶ææåµ51        return method, episode_mae, action_predict, action_true, episode_step_reward  # å°è®ç»å¥½çagentè¿ååºå»52    def prediction(self, method_str, method, state, action, state_kinds=None):53        action_predict = []54        action_true = []55        for i in range(len(state)):56            s = state[i]57            if method_str == "DF-DQN":58                kind = state_kinds[i]59            action_true.append(action[i])60            if method_str == "DQN":61                a, a_value = method.choose_action(state=s, stage="test")62                action_predict.append(a_value)63            elif method_str == "DF-DQN":64                a, a_value = method.choose_action(state=s, kind=kind, stage="test")65                action_predict.append(a_value)66            elif method_str == "DDPG":67                a = method.choose_action(state=s, stage="test")68                a = np.reshape(a, (1, -1))[0][0]69                action_predict.append(a)...

pedometer.py

Source:pedometer.py

...10gz = []  # the gyroscope of z-axis11i_step = []12flag = False1314def  count_step(g_window):15    # applying fft algorithm16    sliding_window = len(g_window)17    T = 0.05   # 50ms18    g_windowf = fft(g_window)19    g_f = 2.0/sliding_window * np.abs(g_windowf[0:sliding_window//2])20    xf = np.linspace(0.0, 1.0/(2.0*T), sliding_window//2)2122    index = np.where(np.logical_and(xf>=0.6, xf<=2))23    index_1 = np.where(np.logical_and(xf>=0, xf<=0.6))24    if index[0].size == 0:25        return 026    else:27        xf_between = xf[index]28        g_windowf_between = g_f[index]29        # g_windowf_between_1 = g_f[index_1]30        if np.mean(g_windowf_between) >= 2.5:31            index_max = np.where(g_windowf_between == max(g_windowf_between))32            count_step = xf_between[index_max[0][0]]*1.233            return count_step34        else:35            return 03637while True:38    cc = str(ser.readline())39    raw_data = cc.split(",")40    if (len(cc)<16) or len(raw_data) != 3:41        continue42    else:43        gz.append(float(raw_data[1]))4445        if len(gz)% 50 == 0:46            flag = True47            i_th_step =  round(count_step(gz))48            i_step.append(i_th_step)49            if i_th_step == 0:50              flag = False51              gz = []52        os.system('cls')53        if flag:54          print("Current Status: Walking")55          print("Step Count:", sum(i_step))56        else:57          print("Current Status: Standing or Typing")
...

Fibonacci, Quantas Chamadas - 1029.py

Source:Fibonacci, Quantas Chamadas - 1029.py

1def fibonacci(n, computed = {0: 0, 1: 1}):2    if n not in computed:3        computed[n] = fibonacci(n-1, computed) + fibonacci(n-2, computed)4    return computed[n]567def count_step_fibonacci(n, count_step = {0: 0, 1: 0, 2: 2}):8    if n not in count_step:9        count_step[n] = count_step_fibonacci(n-1, count_step) + count_step_fibonacci(n-2, count_step) + 210    return count_step[n]111213num = int(input())14inputs = []15for x in range(0, num):16    try:17        read = input()18        if read is '':19            break20        inputs.append(int(read))21    except EOFError:22        break232425for number in inputs:
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