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‎Apress-AI-master/Makefile‎

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PYTHON = python3
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TEST:
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${PYTHON} test_classification.py gen 111
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${PYTHON} test_classification.py margins 111
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${PYTHON} test_blend_multi.py content 122
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${PYTHON} test_blend_multi.py target 122
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${PYTHON} test_blend_multi.py cost 122
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${PYTHON} test_blend_multi.py inventory 122
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${PYTHON} test_blend_multi.py run 122
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${PYTHON} test_gas_blend.py raw 103
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${PYTHON} test_gas_blend.py ref 103
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${PYTHON} test_gas_blend.py run 103
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${PYTHON} test_classification.py gen 111
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${PYTHON} test_classification.py plane 111
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${PYTHON} test_classification.py run 111
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${PYTHON} test_curve_fit.py data 115
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${PYTHON} test_curve_fit.py run 115
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${PYTHON} test_cutting_stock.py data 11
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${PYTHON} test_cutting_stock.py run 11
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${PYTHON} test_cutting_stock.py large 11
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${PYTHON} test_facility_location.py dcost 112
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${PYTHON} test_facility_location.py fcost 112
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${PYTHON} test_facility_location.py run 112
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${PYTHON} test_coexistence.py
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${PYTHON} test_job_shop.py data 129
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${PYTHON} test_job_shop.py run 129
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${PYTHON} test_maxflow.py data 117
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${PYTHON} test_maxflow.py run0 117
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${PYTHON} test_maxflow.py run1 117
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${PYTHON} test_mincost.py data 112
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${PYTHON} test_mincost.py run 112
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${PYTHON} test_diet_problem.py run 12
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${PYTHON} test_multi_commodity_flow.py data 112
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${PYTHON} test_multi_commodity_flow.py run 112
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${PYTHON} test_multi_commodity_flow.py pairs 112
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${PYTHON} test_piecewise.py data 110 250 False
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${PYTHON} test_piecewise.py run 110 250 False
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${PYTHON} test_tricks.py
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${PYTHON} test_piecewise.py ncvx 110 250 False
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${PYTHON} test_tricks_0.py
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${PYTHON} test_tricks_5.py
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${PYTHON} test_tricks_2.py
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${PYTHON} test_tricks_1.py
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${PYTHON} test_tricks_3.py
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${PYTHON} test_piecewise.py data 112 250 True
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${PYTHON} test_piecewise.py run 112 250 True
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${PYTHON} test_piecewise.py run 112 310 True
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${PYTHON} test_piecewise.py run 112 1 True
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${PYTHON} test_piecewise.py non 112 250 True
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${PYTHON} test_piecewise.py data 110 250 False
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${PYTHON} test_piecewise.py run 110 250 False
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${PYTHON} test_project_management.py data 13
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${PYTHON} test_project_management.py run 13
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${PYTHON} test_project_management.py runclp 13
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${PYTHON} test_set_cover.py data 129
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${PYTHON} test_set_cover.py run 129
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${PYTHON} test_set_packing.py data 103
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${PYTHON} test_set_packing.py run 103
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${PYTHON} test_shortest_path.py data 112
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${PYTHON} test_shortest_path.py run 112
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${PYTHON} test_shortest_path.py pm 112
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${PYTHON} test_shortest_path.py tree 112
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${PYTHON} test_shortest_path.py all 112
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${PYTHON} test_sports_timetabling.py data 12
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${PYTHON} test_sports_timetabling.py run 12
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${PYTHON} test_staff_scheduling.py section 11
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${PYTHON} test_staff_scheduling.py instructor 11
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${PYTHON} test_staff_scheduling.py sets 11
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${PYTHON} test_staff_scheduling.py pairs 11
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${PYTHON} test_staff_scheduling.py run 11
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${PYTHON} test_staffing.py data 112
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${PYTHON} test_staffing.py run 112
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${PYTHON} test_staffing.py runo 112
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${PYTHON} test_transship_dist.py data 117
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${PYTHON} test_transship_dist.py run 117
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${PYTHON} test_tsp.py data 118
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${PYTHON} test_tsp.py display 118
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${PYTHON} test_tsp.py path 118
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${PYTHON} test_tsp.py star 118

‎Apress-AI-master/README.md‎

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# Apress-AI
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This is the code described in the book, along with generators of
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random instances and test drivers.
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First, there is a `Makefile` that tests every model in the book. The
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reader should run a `make` before making any change to the code to
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verify that everything is working.
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The only change that may be
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*required* is to change the value of the `PYTHONPATH` variable to
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indicate where is located Google's or-tools. This variable is set on
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the first line of the `Makefile`.
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For each section of the book, the structure is identical. Consider,
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for instance the chapter *Staffing*. The code in the book can be found
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in the file `staffing.py`; in the same file a generator of problem is
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included. And in the file `test_staffing.py` there is a driver that
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generates a problem and solves it. The reader can use this as an example
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on how to use the models.
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‎Apress-AI-master/bin_packing.py‎

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from random import randint,uniform
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from math import ceil
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def gen_data(n):
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R,T=[],0
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for i in range(n):
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RR=[randint(6,10),randint(200,500)]
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T+=RR[0]*RR[1]
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R.append(RR)
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return R,randint(1200, 1500)
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from my_or_tools import newSolver, ObjVal, SolVal
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def solve_model(D,W,symmetry_break=False,knapsack=True):
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s = newSolver('Bin Packing',True)
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nbC,nbP = len(D),sum([P[0] for P in D])
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w = [e for sub in [[d[1]]*d[0] for d in D] for e in sub]
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nbT,nbTmin = bound_trucks(w,W)
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x = [[[s.IntVar(0,1,'') for _ in range(nbT)] \
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for _ in range(d[0])] for d in D]
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y = [s.IntVar(0,1,'') for _ in range(nbT)]
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for k in range(nbT):
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sxk = sum(D[i][1]*x[i][j][k] \
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for i in range(nbC) for j in range(D[i][0]))
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s.Add(sxk <= W*y[k])
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for i in range(nbC):
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for j in range(D[i][0]):
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s.Add(sum([x[i][j][k] for k in range(nbT)]) == 1)
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if symmetry_break:
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for k in range(nbT-1):
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s.Add(y[k] >= y[k+1])
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for i in range(nbC):
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for j in range(D[i][0]):
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for k in range(nbT):
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for jj in range(max(0,j-1),j):
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s.Add(sum(x[i][jj][kk] \
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for kk in range(k+1)) >= x[i][j][k])
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for jj in range(j+1,min(j+2,D[i][0])):
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s.Add(sum(x[i][jj][kk] \
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for kk in range(k,nbT))>=x[i][j][k])
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if knapsack:
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s.Add(sum(W*y[i] for i in range(nbT)) >= sum(w))
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s.Add(sum(y[k] for k in range(nbT)) >= nbTmin)
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s.Minimize(sum(y[k] for k in range(nbT)))
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rc = s.Solve()
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P2T=[[D[i][1], [k for j in range(D[i][0]) for k in range(nbT)
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if SolVal(x[i][j][k])>0]] for i in range(nbC) ]
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T2P=[[k, [(i,j,D[i][1]) \
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for i in range(nbC) for j in range(D[i][0])\
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if SolVal(x[i][j][k])>0]] for k in range(nbT)]
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return rc,ObjVal(s),P2T,T2P
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def bound_trucks(w,W):
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nb,tot = 1,0
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for i in range(len(w)):
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if tot+w[i] < W:
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tot += w[i]
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else:
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tot = w[i]
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nb = nb+1
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return nb,ceil(sum(w)/W)

‎Apress-AI-master/blend_multi.py‎

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from random import randint, uniform
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def gen_data_content(m,n):
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# Oils down, acids accross (more oils than acids m > n)
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R=[]
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for i in range(m):
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RR=[]
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P = 100
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for j in range(n-1):
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if P>1:
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acid = randint(1,min(70,P))*randint(0,1)
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else:
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acid = 0
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RR.append(acid)
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P -= acid
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RR.append(P)
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R.append(RR)
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return R
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def gen_data_target(C):
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F=[]
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R0,R1=[],[]
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m,n=len(C),len(C[0])
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P=100
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R=[0 for j in range(n)]
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for i in range(m-1):
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if P:
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f=randint(1,min(20,P))
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else:
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f=0
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F.append(f)
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P-=f
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for j in range(n):
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acid = f*C[i][j]
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R[j] += acid
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f=P
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F.append(f)
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for j in range(n):
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acid = f*C[m-1][j]
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R[j] += acid
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for j in range(n):
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R0.append((0.95*R[j]/100.0))
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R1.append((1.05*R[j]/100.0))
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return [R0,R1]
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def gen_data_cost(m,k):
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# Oils down, months accross
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R=[]
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for i in range(m):
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RR=[]
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for j in range(k):
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cost = randint(100,200)
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RR.append(cost)
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R.append(RR)
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return R
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def gen_data_inventory(m):
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# Oils down
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R=[]
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for i in range(m):
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cost = [randint(0,200)]
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R.append(cost)
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return R
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from my_or_tools import SolVal, ObjVal, newSolver
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def solve_model(Part,Target,Cost,Inventory,D,SC,SL):
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s = newSolver('Multi-period soap blending problem')
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Oils= range(len(Part))
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Periods, Acids = range(len(Cost[0])), range(len(Part[0]))
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Buy = [[s.NumVar(0,D,'') for _ in Periods] for _ in Oils]
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Blnd = [[s.NumVar(0,D,'') for _ in Periods] for _ in Oils]
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Hold = [[s.NumVar(0,D,'') for _ in Periods] for _ in Oils]
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Prod = [s.NumVar(0,D,'') for _ in Periods]
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CostP= [s.NumVar(0,D*1000,'') for _ in Periods]
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CostS= [s.NumVar(0,D*1000,'') for _ in Periods]
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Acid = [[s.NumVar(0,D*D,'') for _ in Periods] for _ in Acids]
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for i in Oils:
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s.Add(Hold[i][0] == Inventory[i][0])
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for j in Periods:
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s.Add(Prod[j] == sum(Blnd[i][j] for i in Oils))
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s.Add(Prod[j] >= D)
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if j < Periods[-1]:
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for i in Oils:
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s.Add(Hold[i][j]+Buy[i][j]-Blnd[i][j] == Hold[i][j+1])
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s.Add(sum(Hold[i][j] for i in Oils) >= SL[0])
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s.Add(sum(Hold[i][j] for i in Oils) <= SL[1])
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for k in Acids:
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s.Add(Acid[k][j]==sum(Blnd[i][j]*Part[i][k] for i in Oils))
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s.Add(Acid[k][j] >= Target[0][k] * Prod[j])
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s.Add(Acid[k][j] <= Target[1][k] * Prod[j])
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s.Add(CostP[j] == sum(Buy[i][j] * Cost[i][j] for i in Oils))
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s.Add(CostS[j] == sum(Hold[i][j] * SC for i in Oils))
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Cost_product = s.Sum(CostP[j] for j in Periods)
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Cost_storage = s.Sum(CostS[j] for j in Periods)
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s.Minimize(Cost_product+Cost_storage)
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rc = s.Solve()
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B,L,H,A = SolVal(Buy),SolVal(Blnd),SolVal(Hold),SolVal(Acid)
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CP,CS,P = SolVal(CostP),SolVal(CostS),SolVal(Prod)
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return rc,ObjVal(s),B,L,H,P,A,CP,CS

‎Apress-AI-master/coexistence.py‎

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from ortools.linear_solver import pywraplp
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def solve_coexistence():
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t = 'Amphibian coexistence'
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s = pywraplp.Solver(t,pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)
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x = [s.NumVar(0, 1000,'x[%i]' % i) for i in range(3)]
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pop = s.NumVar(0,3000,'pop')
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s.Add(2*x[0] + x[1] + x[2] <= 1500)
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s.Add(x[0] + 3*x[1] + 2*x[2] <= 3000)
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s.Add(x[0] + 2*x[1] + 3*x[2] <= 4000)
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s.Add(pop == x[0] + x[1] + x[2])
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s.Maximize(pop)
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s.Solve()
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return pop.SolutionValue(),[e.SolutionValue() for e in x]

‎Apress-AI-master/curve_fit.py‎

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from random import randint, uniform
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def gen_data(myfunc,n):
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R=[]
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for i in range(n):
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RR=[]
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t = i+uniform(-0.2,0.2)
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RR.append(t)
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RR.append(myfunc(t)*uniform(0.8,1.2))
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R.append(RR)
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return R
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from my_or_tools import ObjVal, SolVal, newSolver
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def solve_model(D,deg=1,objective=0):
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s,n = newSolver('Polynomial fitting'),len(D)
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b = s.infinity()
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a = [s.NumVar(-b,b,'a[%i]' % i) for i in range(1+deg)]
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u = [s.NumVar(0,b,'u[%i]' % i) for i in range(n)]
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v = [s.NumVar(0,b,'v[%i]' % i) for i in range(n)]
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e = s.NumVar(0,b,'e')
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for i in range(n):
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s.Add(D[i][1]==u[i]-v[i]+sum(a[j]*D[i][0]**j \
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for j in range(1+deg)))
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for i in range(n):
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s.Add(u[i] <= e)
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s.Add(v[i] <= e)
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if objective:
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Cost = e
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else:
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Cost = sum(u[i]+v[i] for i in range(n))
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s.Minimize(Cost)
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rc = s.Solve()
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return rc,ObjVal(s),SolVal(a)

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