Problem:


Score : 200 points

Problem Statement

We have held a popularity poll for N items on sale. Item i received A_i votes.

From these N items, we will select M as popular items. However, we cannot select an item with less than \dfrac{1}{4M} of the total number of votes.

If M popular items can be selected, print Yes; otherwise, print No.

Constraints

1 \leq M \leq N \leq 100

1 \leq A_i \leq 1000

A_i are distinct.

All values in input are integers.

Input

Input is given from Standard Input in the following format:

N M
A_1 ... A_N

Output

If M popular items can be selected, print Yes; otherwise, print No.

Sample Input 1

4 1
5 4 2 1

Sample Output 1

Yes

There were 12 votes in total. The most popular item received 5 votes, and we can select it.

Sample Input 2

3 2
380 19 1

Sample Output 2

No

There were 400 votes in total. The second and third most popular items received less than \dfrac{1}{4\times 2} of the total number of votes, so we cannot select them. Thus, we cannot select two popular items.

Sample Input 3

12 3
4 56 78 901 2 345 67 890 123 45 6 789

Sample Output 3

Yes



Sample input:
4 1
5 4 2 1


Sample output:
Yes

Submission status: Runtime Error

Solution code:
import numpy as np

eparams = []
eparams.append(np.array([[0.058814656, 0.03193621, -0.011888318, -0.03248687, -0.061726965, -0.022860432, -0.03243686, 0.07072506, -0.059817977, 0.032853324, -0.05030218, -0.03143452, 0.0007132238, -0.05302827, -0.071716115, -0.02974179, 0.015915, -0.08256117, 0.02046834, -0.0879715, 0.039109122, -0.024474194, 0.09738423, -0.015694227, -0.009362074, -0.038397975, -0.008963789, 0.01974786, 0.030322926, -0.057905696, -0.035329785, -0.080135845, -0.083464175, -0.013657237, -0.01350783, 0.031743642, -0.010524167, -0.017295744, 0.095326565, 0.031280268, 0.06883094, 0.084762245, -0.08680499, -0.010376518, 0.08519528, 0.07358575, 0.051417027, -0.07638214, -0.09795578, -0.07255019, -0.043084677, -0.05807268, 0.08295766, 0.037656557, 0.07523981, 0.09765635, -0.010016018, -0.10824481, 0.06473402, -0.043741606, 0.0619795, 0.099923335, -0.023272853, 0.058091216, 0.08662805, -0.088075124, 0.08819933, 0.095521025, -0.057647802, -0.036065795, -0.024771616, 0.015332323, 0.081676655, 0.033967156, 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eparams.append(np.array([-0.0237325, -0.043705992, 0.03836187, 0.08134823, 0.006872918, -0.008866723, -0.07359702, 0.044307478, 0.04685696, 0.038645573, -0.09352739, 0.043627, -0.0728261, 0.124064475, 0.07707974, -0.016014334, -0.019829622, 0.02530266, 0.008215838, -0.045452427, 0.05084272, -0.038576264, 0.17234972, -0.030053744, -0.07156599, -0.12767173, 0.040335428, -0.022578204, -0.08679177, 0.072820485, 3.4838005e-05, -0.07209901, -0.016629444, 0.07724671, 0.038692303, -0.03665921, 0.08107188, -0.07483169, -0.010753243, 0.074573606, 0.09143293, -0.1711541, 0.03257083, 0.013530212, 0.030227635, -0.07505448, -0.08341935, 0.08218782, -0.054871324, -0.037238713, -0.09921248, -0.0968493, 0.10975076, 0.06104138, -0.09504105, -0.069786094, -0.079443336, 0.06580735, 0.10734674, -0.037890762, -0.07690216, -0.022627339, 0.09190392, 0.014330025, -0.07970752, 0.09854408, -0.10500471, -0.062500834, 0.05530001, -0.03212924, -0.036192026, -0.07517622, 0.06660708, -0.065143466, 0.048043557, -0.029674115, -0.049225543, -0.033664156, -0.046301585, -0.055780172, 0.04735638, -0.039233286, 0.02908542, -0.023882499, 0.11256868, 0.04752343, 0.075328074, -0.081140906, -0.064814866, 0.020838913, -0.06177005, -0.06362531, 0.0028325191, -0.05267601, -0.07414787, 0.0029313462, -0.0206597, -0.07893436, -0.02390924, 0.026135774]))
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eparams.append(np.array([0.052990366, -0.10308473, 0.044666015, 0.058764838, 0.06872144, 0.01756857, 0.053451397, -0.021993937, -0.09505802, 0.018338894, 0.070101395, -0.078194864, -0.06447, 0.028421512, 0.044205002, 0.0028924698, 0.035639867, 0.06972075, 0.081497855, 0.04502731, -0.087088324, -0.002865297, -0.040191818, 0.035950676, 0.07717542, -0.08964188, -0.09959165, -0.097335, 0.021518853, 0.08188785, -0.016026953, -0.048175566, 0.07500365, -0.057761144, -0.03068225, -0.20924263, -0.07584289, 0.06587783, -0.116533116, 0.1076931, 0.10028003, -0.07610464, 0.053089157, 0.099692225, -0.008454776, 0.025513796, 0.048436563, 0.05140708, 0.043889176, 0.07100052, 0.05508846, 0.12971787, -0.0013129067, -0.062597014, -0.10345962, -0.09363887, 0.06673544, -0.059475135, 0.23286322, 0.21679148, -0.0077529335, -0.0058091297, -0.14292277, 0.068616934, 0.010131559, -0.14276645, -0.025807502, -0.0086796405, -0.0032174126, -0.056463838, 0.057263255, 0.016523853, -0.093180075, 0.019254955, 0.06447115, 0.07525955, -0.018747069, 0.012666956, -0.047856845, 0.035874676, 0.07060375, 0.03923801, 0.032685645, -0.050337132, 0.059412364, 0.079221964, 0.01633867, -0.0049799294, -0.08050794, 0.028182857, 0.043742295, -0.047412597, 0.06503826, 0.19498914, 0.10680233, -0.101404175, -0.10356243, -0.09924624, -0.0065476415, 0.0762407]))
eparams.append(np.array([[0.045405794, 0.053482138, 0.10163722, -0.0003407206, -0.0852835, -0.07814179, -0.057211887, -0.00019206989, -0.024879552, 0.047668424, 0.02201171, -0.04081437, 0.033937927, 0.08580668, 0.09110316, 0.027477812, 0.026843438, -0.057943705, 0.09119806, 0.061977968, 0.08060329, 0.06774257, 0.05887997, 0.04066475, -0.061368175, -0.022770466, -0.025241593, -0.02925829, 0.090021454, -0.009392095, -0.08190503, -0.010491857, -0.06817394, -0.14213607, -0.011118209, 0.20338954, -0.06533917, 0.0063340706, -0.017176526, -0.077359535, -0.090490825, 0.012334575, 0.05660907, -0.070524335, 0.09012871, -0.013393383, 0.07407923, -0.070669025, -0.082289346, 0.081365965, -0.06657539, -0.1348316, -0.045257494, -0.080199435, 0.2170607, 0.010473319, 0.062318385, 0.051663455, 0.033154014, -0.14303547, 0.07958067, 0.037711997, 0.011144544, -0.004950147, -0.03729028, 0.05229063, 0.09265689, -0.0011704488, 0.017912347, 0.054817293, 0.07708731, -0.11790073, 0.08001449, -0.07834663, -0.071098156, -0.075337544, -0.064241394, -0.059739847, -0.09294116, -0.12301146, -0.005388619, 0.0014950187, -0.009621896, -0.059269514, -0.05049686, -0.02622725, 0.084786005, 0.038820166, -0.023772517, 0.055650372, 0.035920493, 0.1632656, -0.06355494, -0.045684714, -0.061301973, 0.12449264, 0.040797178, 0.015659688, -0.059994724, 0.015660439], [0.021081157, 0.035438765, -0.18605363, -0.020176612, -0.06409112, 0.02613867, 0.072335124, -0.030475916, -0.05562776, -0.022258764, -0.044496294, 0.060331214, 0.1042653, -0.06432929, -0.07349725, 0.09352435, -0.06797824, -0.04032147, -0.037891235, 0.08082953, 0.017080644, -0.018769963, 0.010440076, -0.02929828, -0.028246526, -0.046348743, -0.07583719, -0.06409753, -0.024844704, 0.08074216, -0.037032545, -0.031540368, -0.045975294, 0.0075229662, 0.043868814, -0.09044456, 0.18728285, -0.020079223, -0.06025362, 0.10845549, 0.082956694, -0.021208145, 0.024397252, -0.014857126, 0.02589101, 0.03346993, 0.08016771, 0.02082422, 0.02644677, -0.078564964, 0.06781928, 0.01628337, -0.0074768704, -0.05746404, -0.08848352, -0.037724245, 0.03777358, -0.057411887, 0.10017648, -0.022484684, 0.04802732, -0.054031506, -0.04664199, -0.03346854, -0.017570551, -0.109328, 0.010657776, -0.07657766, 0.078580715, -0.07538932, -0.033671077, 0.025683763, -0.07153225, -0.03148593, -0.026030239, -0.015192115, -0.041008197, 0.06067849, -0.04059154, 0.13836527, -0.08960119, -0.050724953, 0.1014578, -0.09719481, -0.0036420324, -0.08725446, -0.034337733, -0.045757398, -0.08449768, 0.027367994, -0.013810629, 0.017089562, -0.016310418, 0.17899562, -0.010851684, 0.037597958, 0.02826332, 0.011728861, 0.051761925, 0.055961803]]))
eparams.append(np.array([-0.08743197, -0.052482188]))

def relu(x):
    return np.maximum(0,x)

def predict(x):
    if x[0]>=x[1]:
        return 0
    else:
        return 1

def emodel(x):
    x = eparams[0].dot(np.array(x))
    x = x + eparams[1]
    x = relu(x)
    x = eparams[2].dot(np.array(x))
    x = x + eparams[3]
    x = relu(x)
    x = eparams[4].dot(np.array(x))
    x = x + eparams[5]
    return x

N,M = list(map(int,input().split()))
A = list(map(int,input().split()))

x = np.concatenate([np.array(A), np.zeros(100-N), np.array(M)])

if predict(emodel(x))==1:
    print("Yes")
else:
    print("No")