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phd v1 of calculation
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@Deer-WarLord
Deer-WarLord committed Nov 28, 2018
1 parent d150e17 commit 984a357
Showing 1 changed file with 21 additions and 99 deletions.
120 changes: 21 additions & 99 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
import json
import leather
from pprint import pprint
import numpy as np


class RearmingSimulation:
Expand All @@ -13,28 +14,28 @@ def __init__(self):
json_initial_data = json.loads(initial_data)

self.C = float(json_initial_data["C"])
self.L_old_0 = json_initial_data["L_old_0"]
self.K_old_0 = [float(item) for item in json_initial_data["K_old_0"]]
self.theta_old = [float(item) for item in json_initial_data["theta_old"]]
self.S_phase_2 = [float(item) for item in json_initial_data["S_phase_2"]]
self.S_phase_1 = [float(item) for item in json_initial_data["S_phase_1"]]
self.S_tilda = [float(item) for item in json_initial_data["S_tilda"]]
self.L_old_0 = np.array(json_initial_data["L_old_0"])
self.K_old_0 = np.array([float(item) for item in json_initial_data["K_old_0"]])
self.theta_old = np.array([float(item) for item in json_initial_data["theta_old"]])
self.S_phase_2 = np.array([float(item) for item in json_initial_data["S_phase_2"]])
self.S_phase_1 = np.array([float(item) for item in json_initial_data["S_phase_1"]])
self.S_tilda = np.array([float(item) for item in json_initial_data["S_tilda"]])
self.tau = json_initial_data["tau"]
self.mu = [float(item) for item in json_initial_data["mu"]]
self.mu = np.array([float(item) for item in json_initial_data["mu"]])
self.nu = float(json_initial_data["nu"])
self.L0 = json_initial_data["L0"]

self.betta_old = [float(item) for item in json_initial_data["betta_old"]]
self.alpha_old = [float(item) for item in json_initial_data["alpha_old"]]
self.A_old = [float(item) for item in json_initial_data["A_old"]]
self.betta_old = np.array([float(item) for item in json_initial_data["betta_old"]])
self.alpha_old = np.array([float(item) for item in json_initial_data["alpha_old"]])
self.A_old = np.array([float(item) for item in json_initial_data["A_old"]])

self.betta_new = [float(item) for item in json_initial_data["betta_new"]]
self.alpha_new = [float(item) for item in json_initial_data["alpha_new"]]
self.A_new = [float(item) for item in json_initial_data["A_new"]]
self.betta_new = np.array([float(item) for item in json_initial_data["betta_new"]])
self.alpha_new = np.array([float(item) for item in json_initial_data["alpha_new"]])
self.A_new = np.array([float(item) for item in json_initial_data["A_new"]])

self.k_new = [float(item) for item in json_initial_data["k_new"]]
self.k_new = np.array([float(item) for item in json_initial_data["k_new"]])

self.a = [float(item) for item in json_initial_data["a"]]
self.a = np.array([float(item) for item in json_initial_data["a"]])

self.eps = float(json_initial_data["eps"])
self.dh = float(json_initial_data["dh"])
Expand All @@ -51,9 +52,9 @@ def __init__(self):
"total should be less then 1. Now %s" % total)

self.accumulations = {}
self.K_new_0 = [0.0, 0.0, 0.0]
self.L_new_0 = [0, 0, 0]
self.Theta_new_prev = [0.0, 0.0, 0.0]
self.K_new_0 = np.array([0.0, 0.0, 0.0])
self.L_new_0 = np.array([0, 0, 0])
self.Theta_new_prev = np.array([0.0, 0.0, 0.0])
self.sum_s_underline = 0.8
self.sum_s_tilda = 0.2

Expand Down Expand Up @@ -152,91 +153,12 @@ def score(self, prev_vector, current_vector, diff):
return result

def phase_1(self):
s = []

K_old_prev = self.K_old_0
# K_1 = - self.mu * self.K_old_0 + s[0]*
L_old_prev = self.L_old_0

result_vectors = {
0: (K_old_prev, L_old_prev, 0, self.Xi_old(2, K_old_prev[2],
L_old_prev[2]), [0.4, 0.4, 0.0])
}

for t in self.xfrange(1, self.tau + 1, self.dh):

is_found = False
investment = self.Xi_old(1, K_old_prev[1], L_old_prev[1])
self.accumulations[t] = investment
L_old = [int(self.theta_old[i] * self.L_general(t)) for i in range(0, 3)]
sum_L_old = sum(L_old)
score_results = {}

for S_phase_1 in self.generate_s(self.ds, self.sum_s_underline):

K_old = [self.captial_old(i, K_old_prev[i], t, self.dh,
K_old_prev[1], L_old_prev[1], S_phase_1[i]) for i in
range(0, 3)]

X = [self.Xi_old(i, K_old[i], L_old[i]) for i in range(0, 3)]

if self.matirial_condition(X, sum_L_old):
score_results[S_phase_1] = (self.score_old(X), K_old, X[2])
is_found = True

if not is_found:
raise Exception("The investments vector wasn't found on iteration %d" % t)

print("Phase 1 iteration %.2f completed " % t)

max_score = 0
for s_vector, (score, K, X2) in score_results.items():
if score > max_score:
result_vectors[t] = (K, L_old, investment, X2, s_vector)
max_score = score

K_old_prev, L_old_prev = result_vectors[t][0], L_old

grid = leather.Grid()

chart = leather.Chart('Capital Phase 1')
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][0][0], name="Sector 0")
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][0][1], name="Sector 1")
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][0][2], name="Sector 2")
grid.add_one(chart)

chart = leather.Chart('Employees Phase 1')
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][1][0], name="Sector 0")
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][1][1], name="Sector 1")
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][1][2], name="Sector 2")
grid.add_one(chart)

chart = leather.Chart('X1_X2')
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][2], name="X1")
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][3], name="X2")
chart.add_line([(t, self.C) for t in range(0, self.tau + 1)],
x=lambda row, i: row[0], y=lambda row, i: row[1],
name="C")
grid.add_one(chart)

chart = leather.Chart('Share of the investments')
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][4][0], name="Sector 0")
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][4][1], name="Sector 1")
chart.add_line(result_vectors.items(), x=lambda row, i: row[0],
y=lambda row, i: row[1][4][2], name="Sector 2")
grid.add_one(chart)

grid.to_svg('results_phase_1.svg')

return K_old_prev

def phase_2(self, K_old_prev):
K_new_prev = self.K_new_0
Expand Down

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