Some changes w.r.t lists

examples/list_tests.nbt

@@ -0,0 +1,14 @@
+let xs = [1, 2, 3]
+
+#assert(len([]) == 0)
+# assert(len(xs) == 3)
+
+# assert(head(xs) == 1)
+# assert(tail(xs) == [2, 3])
+
+assert(sequence(5) == [0, 1, 2, 3, 4])
+assert(generate(3, const_5) == [5, 5, 5])
+assert(map(inc, [1, 2, 3]) == [2, 3, 4])
+assert(reverse([1, 2, 3]) == [3, 2, 1])
+assert(foldl(add, 0, [1, 2, 3, 4, 5]) == 15)
+assert(foldl(mul, 1, [1, 2, 3, 4, 5]) == 120)

numbat/modules/core/lists.nbt

@@ -7,17 +7,16 @@ fn cons<A>(x: A, xs: List<A>) -> List<A>
 
 fn is_empty<A>(xs: List<A>) -> Bool = len(xs) == 0
 
-# We definitely want to make the following functions generic, but this is not yet possible:
-# Also, we want the base case to be the empty list, but this is also not yet possible.
+# We definitely want to make the following functions generic, but this is not yet possible.
 
 fn generate(n: Scalar, f: Fn[() -> Scalar]) -> List<Scalar> =
-  if n == 1
-    then [f()]
+  if n == 0
+    then []
     else cons(f(), generate(n - 1, f))
 
 fn map(f: Fn[(Scalar) -> Scalar], xs: List<Scalar>) -> List<Scalar> =
-  if len(xs) == 1
-    then [f(head(xs))]
+  if len(xs) == 0
+    then []
     else cons(f(head(xs)), map(f, tail(xs)))
 
 fn cons_end(xs: List<Scalar>, x: Scalar) -> List<Scalar> =
@@ -26,8 +25,8 @@ fn cons_end(xs: List<Scalar>, x: Scalar) -> List<Scalar> =
     else cons(head(xs), cons_end(tail(xs), x))
 
 fn reverse(xs: List<Scalar>) -> List<Scalar> =
-  if len(xs) == 1
-    then xs
+  if len(xs) == 0
+    then []
     else cons_end(reverse(tail(xs)), head(xs))
 
 fn sequence(n: Scalar) -> List<Scalar> =
@@ -46,10 +45,3 @@ fn inc(x: Scalar) -> Scalar = x + 1
 
 fn add(x: Scalar, y: Scalar) -> Scalar = x + y
 fn mul(x: Scalar, y: Scalar) -> Scalar = x * y
-
-assert(sequence(5) == [0, 1, 2, 3, 4])
-assert(generate(3, const_5) == [5, 5, 5])
-assert(map(inc, [1, 2, 3]) == [2, 3, 4])
-assert(reverse([1, 2, 3]) == [3, 2, 1])
-assert(foldl(add, 0, [1, 2, 3, 4, 5]) == 15)
-assert(foldl(mul, 1, [1, 2, 3, 4, 5]) == 120)

numbat/modules/core/random.nbt

@@ -8,95 +8,95 @@ use math::functions
 @description("Uniformly samples the interval [0,1).")
 fn random() -> Scalar
 
-@name("Continuous uniform distribution sampling")
-@url("https://en.wikipedia.org/wiki/Continuous_uniform_distribution")
-@description("Uniformly samples the interval [a,b) if a<=b or [b,a) if b<a using inversion sampling.")
-fn rand_uniform<T>(a: T, b: T) -> T =
-    if a <= b
-    then random() * (b - a) + a
-    else random() * (a - b) + b
+# @name("Continuous uniform distribution sampling")
+# @url("https://en.wikipedia.org/wiki/Continuous_uniform_distribution")
+# @description("Uniformly samples the interval [a,b) if a<=b or [b,a) if b<a using inversion sampling.")
+# fn rand_uniform<T>(a: T, b: T) -> T =
+#     if a <= b
+#     then random() * (b - a) + a
+#     else random() * (a - b) + b
 
-@name("Discrete uniform distribution sampling")
-@url("https://en.wikipedia.org/wiki/Discrete_uniform_distribution")
-@description("Uniformly samples the integers in the interval [a, b].")
-fn rand_int(a: Scalar, b: Scalar) -> Scalar =
-    if a <= b
-    then floor( random() * (floor(b) - ceil(a) + 1) ) + ceil(a)
-    else floor( random() * (floor(a) - ceil(b) + 1) ) + ceil(b)
+# @name("Discrete uniform distribution sampling")
+# @url("https://en.wikipedia.org/wiki/Discrete_uniform_distribution")
+# @description("Uniformly samples the integers in the interval [a, b].")
+# fn rand_int(a: Scalar, b: Scalar) -> Scalar =
+#     if a <= b
+#     then floor( random() * (floor(b) - ceil(a) + 1) ) + ceil(a)
+#     else floor( random() * (floor(a) - ceil(b) + 1) ) + ceil(b)
 
-@name("Bernoulli distribution sampling")
-@url("https://en.wikipedia.org/wiki/Bernoulli_distribution")
-@description("Samples a Bernoulli random variable, that is, 1 with probability p, 0 with probability 1-p.
-              Parameter p must be a probability (0 <= p <= 1).")
-fn rand_bernoulli(p: Scalar) -> Scalar =
-    if p>=0 && p<=1
-    then (if random() < p
-        then 1
-        else 0)
-    else error("Argument p must be a probability (0 <= p <= 1).")
+# @name("Bernoulli distribution sampling")
+# @url("https://en.wikipedia.org/wiki/Bernoulli_distribution")
+# @description("Samples a Bernoulli random variable, that is, 1 with probability p, 0 with probability 1-p.
+#               Parameter p must be a probability (0 <= p <= 1).")
+# fn rand_bernoulli(p: Scalar) -> Scalar =
+#     if p>=0 && p<=1
+#     then (if random() < p
+#         then 1
+#         else 0)
+#     else error("Argument p must be a probability (0 <= p <= 1).")
 
-@name("Binomial distribution sampling")
-@url("https://en.wikipedia.org/wiki/Binomial_distribution")
-@description("Samples a binomial distribution by doing n Bernoulli trials with probability p.
-              Parameter n must be a positive integer, parameter p must be a probability (0 <= p <= 1).")
-fn rand_binom(n: Scalar, p: Scalar) -> Scalar =
-    if n >= 1
-    then rand_binom(n-1, p) + rand_bernoulli(p)
-    else if n == 0
-    then 0
-    else error("Argument n must be a positive integer.")
+# @name("Binomial distribution sampling")
+# @url("https://en.wikipedia.org/wiki/Binomial_distribution")
+# @description("Samples a binomial distribution by doing n Bernoulli trials with probability p.
+#               Parameter n must be a positive integer, parameter p must be a probability (0 <= p <= 1).")
+# fn rand_binom(n: Scalar, p: Scalar) -> Scalar =
+#     if n >= 1
+#     then rand_binom(n-1, p) + rand_bernoulli(p)
+#     else if n == 0
+#     then 0
+#     else error("Argument n must be a positive integer.")
 
-@name("Normal distribution sampling")
-@url("https://en.wikipedia.org/wiki/Normal_distribution")
-@description("Samples a normal distribution with mean μ and standard deviation σ using the Box-Muller transform.")
-fn rand_norm<T>(μ: T, σ: T) -> T =
-    μ + sqrt(-2 σ² × ln(random())) × sin(2π × random())
+# @name("Normal distribution sampling")
+# @url("https://en.wikipedia.org/wiki/Normal_distribution")
+# @description("Samples a normal distribution with mean μ and standard deviation σ using the Box-Muller transform.")
+# fn rand_norm<T>(μ: T, σ: T) -> T =
+#     μ + sqrt(-2 σ² × ln(random())) × sin(2π × random())
 
-@name("Geometric distribution sampling")
-@url("https://en.wikipedia.org/wiki/Geometric_distribution")
-@description("Samples a geometric distribution (the distribution of the number of Bernoulli trials with probability p needed to get one success) by inversion sampling.
-              Parameter p must be a probability (0 <= p <= 1).")
-fn rand_geom(p: Scalar) -> Scalar =
-    if p>=0 && p<=1
-    then ceil( ln(1-random()) / ln(1-p) )
-    else error("Argument p must be a probability (0 <= p <= 1).")
+# @name("Geometric distribution sampling")
+# @url("https://en.wikipedia.org/wiki/Geometric_distribution")
+# @description("Samples a geometric distribution (the distribution of the number of Bernoulli trials with probability p needed to get one success) by inversion sampling.
+#               Parameter p must be a probability (0 <= p <= 1).")
+# fn rand_geom(p: Scalar) -> Scalar =
+#     if p>=0 && p<=1
+#     then ceil( ln(1-random()) / ln(1-p) )
+#     else error("Argument p must be a probability (0 <= p <= 1).")
 
-# A helper function for rand_poisson, counts how many samples of the standard uniform distribution need to be multiplied to fall below lim.
-fn _poisson(lim: Scalar, prod: Scalar) -> Scalar =
-    if prod > lim
-    then _poisson(lim,  prod × random()) + 1
-    else -1
+# # A helper function for rand_poisson, counts how many samples of the standard uniform distribution need to be multiplied to fall below lim.
+# fn _poisson(lim: Scalar, prod: Scalar) -> Scalar =
+#     if prod > lim
+#     then _poisson(lim,  prod × random()) + 1
+#     else -1
 
-@name("Poisson distribution sampling")
-@url("https://en.wikipedia.org/wiki/Poisson_distribution")
-@description("Sampling a poisson distribution with rate λ, that is, the distribution of the number of events occurring in a fixed interval if these events occur with mean rate λ.
-              The rate parameter λ must not be negative.")
-# This implementation is based on the exponential distribution of inter-arrival times. For details see L. Devroye, Non-Uniform Random Variate Generation, p. 504, Lemma 3.3.
-fn rand_poisson(λ: Scalar) -> Scalar =
-    if λ >= 0
-    then _poisson(exp(-λ), 1)
-    else error("Argument λ must not be negative.")
+# @name("Poisson distribution sampling")
+# @url("https://en.wikipedia.org/wiki/Poisson_distribution")
+# @description("Sampling a poisson distribution with rate λ, that is, the distribution of the number of events occurring in a fixed interval if these events occur with mean rate λ.
+#               The rate parameter λ must not be negative.")
+# # This implementation is based on the exponential distribution of inter-arrival times. For details see L. Devroye, Non-Uniform Random Variate Generation, p. 504, Lemma 3.3.
+# fn rand_poisson(λ: Scalar) -> Scalar =
+#     if λ >= 0
+#     then _poisson(exp(-λ), 1)
+#     else error("Argument λ must not be negative.")
 
-@name("Exponential distribution sampling")
-@url("https://en.wikipedia.org/wiki/Exponential_distribution")
-@description("Sampling an exponential distribution (the distribution of the distance between events in a Poisson process with rate λ) using inversion sampling.
-              The rate parameter λ must be positive.")
-fn rand_expon<T>(λ: T) -> 1/T =
-    if value_of(λ) > 0
-    then - ln(1-random()) / λ
-    else error("Argument λ must be positive.")
+# @name("Exponential distribution sampling")
+# @url("https://en.wikipedia.org/wiki/Exponential_distribution")
+# @description("Sampling an exponential distribution (the distribution of the distance between events in a Poisson process with rate λ) using inversion sampling.
+#               The rate parameter λ must be positive.")
+# fn rand_expon<T>(λ: T) -> 1/T =
+#     if value_of(λ) > 0
+#     then - ln(1-random()) / λ
+#     else error("Argument λ must be positive.")
 
-@name("Log-normal distribution sampling")
-@url("https://en.wikipedia.org/wiki/Log-normal_distribution")
-@description("Sampling a log-normal distribution, that is, a distribution whose log is a normal distribution with mean μ and standard deviation σ.")
-fn rand_lognorm(μ: Scalar, σ: Scalar) -> Scalar =
-    exp( μ + σ × rand_norm(0, 1) )
+# @name("Log-normal distribution sampling")
+# @url("https://en.wikipedia.org/wiki/Log-normal_distribution")
+# @description("Sampling a log-normal distribution, that is, a distribution whose log is a normal distribution with mean μ and standard deviation σ.")
+# fn rand_lognorm(μ: Scalar, σ: Scalar) -> Scalar =
+#     exp( μ + σ × rand_norm(0, 1) )
 
-@name("Pareto distribution sampling")
-@url("https://en.wikipedia.org/wiki/Pareto_distribution")
-@description("Sampling a Pareto distribution with minimum value min and shape parameter α using inversion sampling.
-              Both parameters α and min must be positive.")
-fn rand_pareto<T>(α: Scalar, min: T) -> T =
-    if value_of(min) > 0 && α > 0
-    then min / ((1-random())^(1/α))
-    else error("Both arguments α and min must be positive.")
+# @name("Pareto distribution sampling")
+# @url("https://en.wikipedia.org/wiki/Pareto_distribution")
+# @description("Sampling a Pareto distribution with minimum value min and shape parameter α using inversion sampling.
+#               Both parameters α and min must be positive.")
+# fn rand_pareto<T>(α: Scalar, min: T) -> T =
+#     if value_of(min) > 0 && α > 0
+#     then min / ((1-random())^(1/α))
+#     else error("Both arguments α and min must be positive.")

numbat/modules/prelude.nbt

@@ -5,7 +5,7 @@ use core::functions
 use core::lists
 use core::strings
 use core::error
-#use core::random
+use core::random
 
 use math::constants
 use math::functions