def cumsum(a: ndarray, axis: tp.Optional[int] = None, dtype: tp.Optional[np.generic] = None, out: tp.Optional[ndarray] = None) \ -> tp.Union[float, ndarray]: """ Return the cumulative sum of the elements along a given axis. """ if axis is None: flat_array = a.flatten() new_af_array = af.accum(flat_array._af_array) else: new_af_array = af.accum(a._af_array, dim=axis) return ndarray(new_af_array)
def simple_algorithm(verbose = False): display_func = _util.display_func(verbose) print_func = _util.print_func(verbose) a = af.randu(3, 3) print_func(af.sum(a), af.product(a), af.min(a), af.max(a), af.count(a), af.any_true(a), af.all_true(a)) display_func(af.sum(a, 0)) display_func(af.sum(a, 1)) display_func(af.product(a, 0)) display_func(af.product(a, 1)) display_func(af.min(a, 0)) display_func(af.min(a, 1)) display_func(af.max(a, 0)) display_func(af.max(a, 1)) display_func(af.count(a, 0)) display_func(af.count(a, 1)) display_func(af.any_true(a, 0)) display_func(af.any_true(a, 1)) display_func(af.all_true(a, 0)) display_func(af.all_true(a, 1)) display_func(af.accum(a, 0)) display_func(af.accum(a, 1)) display_func(af.sort(a, is_ascending=True)) display_func(af.sort(a, is_ascending=False)) b = (a > 0.1) * a c = (a > 0.4) * a d = b / c print_func(af.sum(d)); print_func(af.sum(d, nan_val=0.0)); display_func(af.sum(d, dim=0, nan_val=0.0)); val,idx = af.sort_index(a, is_ascending=True) display_func(val) display_func(idx) val,idx = af.sort_index(a, is_ascending=False) display_func(val) display_func(idx) b = af.randu(3,3) keys,vals = af.sort_by_key(a, b, is_ascending=True) display_func(keys) display_func(vals) keys,vals = af.sort_by_key(a, b, is_ascending=False) display_func(keys) display_func(vals) c = af.randu(5,1) d = af.randu(5,1) cc = af.set_unique(c, is_sorted=False) dd = af.set_unique(af.sort(d), is_sorted=True) display_func(cc) display_func(dd) display_func(af.set_union(cc, dd, is_unique=True)) display_func(af.set_union(cc, dd, is_unique=False)) display_func(af.set_intersect(cc, cc, is_unique=True)) display_func(af.set_intersect(cc, cc, is_unique=False))
def _movavg_af(X, N, axis=0): """ Calculate moving average over N samples using arrayfire """ cs = af.accum(X, dim=axis) return cs[N:] - cs[:-N]
af.display(af.min(a, 0)) af.display(af.min(a, 1)) af.display(af.max(a, 0)) af.display(af.max(a, 1)) af.display(af.count(a, 0)) af.display(af.count(a, 1)) af.display(af.any_true(a, 0)) af.display(af.any_true(a, 1)) af.display(af.all_true(a, 0)) af.display(af.all_true(a, 1)) af.display(af.accum(a, 0)) af.display(af.accum(a, 1)) af.display(af.sort(a, is_ascending=True)) af.display(af.sort(a, is_ascending=False)) val, idx = af.sort_index(a, is_ascending=True) af.display(val) af.display(idx) val, idx = af.sort_index(a, is_ascending=False) af.display(val) af.display(idx) b = af.randu(3, 3) keys, vals = af.sort_by_key(a, b, is_ascending=True) af.display(keys)
af.print_array(af.min(a, 0)) af.print_array(af.min(a, 1)) af.print_array(af.max(a, 0)) af.print_array(af.max(a, 1)) af.print_array(af.count(a, 0)) af.print_array(af.count(a, 1)) af.print_array(af.any_true(a, 0)) af.print_array(af.any_true(a, 1)) af.print_array(af.all_true(a, 0)) af.print_array(af.all_true(a, 1)) af.print_array(af.accum(a, 0)) af.print_array(af.accum(a, 1)) af.print_array(af.sort(a, is_ascending=True)) af.print_array(af.sort(a, is_ascending=False)) val,idx = af.sort_index(a, is_ascending=True) af.print_array(val) af.print_array(idx) val,idx = af.sort_index(a, is_ascending=False) af.print_array(val) af.print_array(idx) b = af.randu(3,3) keys,vals = af.sort_by_key(a, b, is_ascending=True) af.print_array(keys)
af.display(af.min(a, 0)) af.display(af.min(a, 1)) af.display(af.max(a, 0)) af.display(af.max(a, 1)) af.display(af.count(a, 0)) af.display(af.count(a, 1)) af.display(af.any_true(a, 0)) af.display(af.any_true(a, 1)) af.display(af.all_true(a, 0)) af.display(af.all_true(a, 1)) af.display(af.accum(a, 0)) af.display(af.accum(a, 1)) af.display(af.sort(a, is_ascending=True)) af.display(af.sort(a, is_ascending=False)) val,idx = af.sort_index(a, is_ascending=True) af.display(val) af.display(idx) val,idx = af.sort_index(a, is_ascending=False) af.display(val) af.display(idx) b = af.randu(3,3) keys,vals = af.sort_by_key(a, b, is_ascending=True) af.display(keys)
def simple_algorithm(verbose=False): display_func = _util.display_func(verbose) print_func = _util.print_func(verbose) a = af.randu(3, 3) k = af.constant(1, 3, 3, dtype=af.Dtype.u32) af.eval(k) print_func(af.sum(a), af.product(a), af.min(a), af.max(a), af.count(a), af.any_true(a), af.all_true(a)) display_func(af.sum(a, 0)) display_func(af.sum(a, 1)) rk = af.constant(1, 3, dtype=af.Dtype.u32) rk[2] = 0 af.eval(rk) display_func(af.sumByKey(rk, a, dim=0)) display_func(af.sumByKey(rk, a, dim=1)) display_func(af.productByKey(rk, a, dim=0)) display_func(af.productByKey(rk, a, dim=1)) display_func(af.minByKey(rk, a, dim=0)) display_func(af.minByKey(rk, a, dim=1)) display_func(af.maxByKey(rk, a, dim=0)) display_func(af.maxByKey(rk, a, dim=1)) display_func(af.anyTrueByKey(rk, a, dim=0)) display_func(af.anyTrueByKey(rk, a, dim=1)) display_func(af.allTrueByKey(rk, a, dim=0)) display_func(af.allTrueByKey(rk, a, dim=1)) display_func(af.countByKey(rk, a, dim=0)) display_func(af.countByKey(rk, a, dim=1)) display_func(af.product(a, 0)) display_func(af.product(a, 1)) display_func(af.min(a, 0)) display_func(af.min(a, 1)) display_func(af.max(a, 0)) display_func(af.max(a, 1)) display_func(af.count(a, 0)) display_func(af.count(a, 1)) display_func(af.any_true(a, 0)) display_func(af.any_true(a, 1)) display_func(af.all_true(a, 0)) display_func(af.all_true(a, 1)) display_func(af.accum(a, 0)) display_func(af.accum(a, 1)) display_func(af.scan(a, 0, af.BINARYOP.ADD)) display_func(af.scan(a, 1, af.BINARYOP.MAX)) display_func(af.scan_by_key(k, a, 0, af.BINARYOP.ADD)) display_func(af.scan_by_key(k, a, 1, af.BINARYOP.MAX)) display_func(af.sort(a, is_ascending=True)) display_func(af.sort(a, is_ascending=False)) b = (a > 0.1) * a c = (a > 0.4) * a d = b / c print_func(af.sum(d)) print_func(af.sum(d, nan_val=0.0)) display_func(af.sum(d, dim=0, nan_val=0.0)) val, idx = af.sort_index(a, is_ascending=True) display_func(val) display_func(idx) val, idx = af.sort_index(a, is_ascending=False) display_func(val) display_func(idx) b = af.randu(3, 3) keys, vals = af.sort_by_key(a, b, is_ascending=True) display_func(keys) display_func(vals) keys, vals = af.sort_by_key(a, b, is_ascending=False) display_func(keys) display_func(vals) c = af.randu(5, 1) d = af.randu(5, 1) cc = af.set_unique(c, is_sorted=False) dd = af.set_unique(af.sort(d), is_sorted=True) display_func(cc) display_func(dd) display_func(af.set_union(cc, dd, is_unique=True)) display_func(af.set_union(cc, dd, is_unique=False)) display_func(af.set_intersect(cc, cc, is_unique=True)) display_func(af.set_intersect(cc, cc, is_unique=False))